@article{Zhu,
author = {Zhu, Bo and Liu, Jeremiah Z. and Rosen, Bruce R. and Rosen, Matthew S.},
file = {:C\:/Users/attil/Downloads/1704.08841.pdf:pdf},
journal = {Nature Medicine},
title = {{Image reconstruction by domain transform manifold learning}},
year = {2018}
}
@article{Brock2021,
abstract = {Batch normalization is a key component of most image classification models, but it has many undesirable properties stemming from its dependence on the batch size and interactions between examples. Although recent work has succeeded in training deep ResNets without normalization layers, these models do not match the test accuracies of the best batch-normalized networks, and are often unstable for large learning rates or strong data augmentations. In this work, we develop an adaptive gradient clipping technique which overcomes these instabilities, and design a significantly improved class of Normalizer-Free ResNets. Our smaller models match the test accuracy of an EfficientNet-B7 on ImageNet while being up to 8.7x faster to train, and our largest models attain a new state-of-the-art top-1 accuracy of 86.5%. In addition, Normalizer-Free models attain significantly better performance than their batch-normalized counterparts when finetuning on ImageNet after large-scale pre-training on a dataset of 300 million labeled images, with our best models obtaining an accuracy of 89.2%. Our code is available at https://github.com/deepmind/ deepmind-research/tree/master/nfnets},
archivePrefix = {arXiv},
arxivId = {2102.06171},
author = {Brock, Andrew and De, Soham and Smith, Samuel L. and Simonyan, Karen},
eprint = {2102.06171},
file = {:C\:/Users/attil/Downloads/2102.06171.pdf:pdf},
title = {{High-Performance Large-Scale Image Recognition Without Normalization}},
url = {http://arxiv.org/abs/2102.06171},
year = {2021}
}
@article{Bylund2018,
author = {Bylund, Mikael and Brynolfsson, Patrik and Garpebring, Anders and Nyholm, Tufve and Jonsson, Joakim and Tommy, L},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Bylund et al. - 2018 - Deep Learning for Synthetic CT Generation.pdf:pdf},
title = {{Deep Learning for Synthetic CT Generation}},
year = {2018}
}
@article{Fedus2018,
abstract = {Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion. Unlike other generative models, the data distribution is learned via a game between a generator (the generative model) and a discriminator (a teacher providing training signal) that each minimize their own cost. GANs are designed to reach a Nash equilibrium at which each player cannot reduce their cost without changing the other players' parameters. One useful approach for the theory of GANs is to show that a divergence between the training distribution and the model distribution obtains its minimum value at equilibrium. Several recent research directions have been motivated by the idea that this divergence is the primary guide for the learning process and that every step of learning should decrease the divergence. We show that this view is overly restrictive. During GAN training, the discriminator provides learning signal in situations where the gradients of the divergences between distributions would not be useful. We provide empirical counterexamples to the view of GAN training as divergence minimization. Specifically, we demonstrate that GANs are able to learn distributions in situations where the divergence minimization point of view predicts they would fail. We also show that gradient penalties motivated from the divergence minimization perspective are equally helpful when applied in other contexts in which the divergence minimization perspective does not predict they would be helpful. This contributes to a growing body of evidence that GAN training may be more usefully viewed as approaching Nash equilibria via trajectories that do not necessarily minimize a specific divergence at each step.},
archivePrefix = {arXiv},
arxivId = {arXiv:1710.08446v3},
author = {Fedus, William and Rosca, Mihaela and Lakshminarayanan, Balaji and Dai, Andrew M. and Mohamed, Shakir and Goodfellow, Ian},
eprint = {arXiv:1710.08446v3},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Fedus et al. - 2018 - Many paths to equilibrium Gans do not need to decrease a divergence at every step.pdf:pdf},
journal = {6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings},
pages = {1--21},
title = {{Many paths to equilibrium: Gans do not need to decrease a divergence at every step}},
year = {2018}
}
@book{McRobbie2006,
author = {McRobbie, Donald W. and Moore, Elizabeth A. and Graves, Martin J. and Prince, Martin R.},
booktitle = {MRI from Picture to Proton},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/McRobbie et al. - 2006 - MRI from picture to proton.pdf:pdf},
pages = {1--397},
title = {{MRI from picture to proton}},
year = {2006}
}
@article{Miyato2018a,
abstract = {We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) 1000-class image dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator. The code with Chainer (Tokui et al., 2015), generated images and pretrained models are available at https://github.com/pfnet-research/sngan_projection.},
archivePrefix = {arXiv},
arxivId = {arXiv:1802.05637v2},
author = {Miyato, Takeru and Koyama, Masanori},
eprint = {arXiv:1802.05637v2},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Miyato, Koyama - 2018 - Cgans with projection discriminator.pdf:pdf},
journal = {arXiv},
title = {{Cgans with projection discriminator}},
year = {2018}
}
@article{Vu2019,
archivePrefix = {arXiv},
arxivId = {arXiv:1910.05338v1},
author = {Vu, Minh H and Nyholm, Tufve and Tommy, L},
doi = {10.13140/RG.2.2.20358.52803},
eprint = {arXiv:1910.05338v1},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Vu, Nyholm, Tommy - 2019 - TuNet End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks.pdf:pdf},
number = {October},
title = {{TuNet : End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks}},
year = {2019}
}
@article{Li2017,
abstract = {Neural network training relies on our ability to find "good" minimizers of highly non-convex loss functions. It is well-known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and well-chosen training parameters (batch size, learning rate, optimizer) produce minimizers that generalize better. However, the reasons for these differences, and their effects on the underlying loss landscape, are not well understood. In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple "filter normalization" method that helps us visualize loss function curvature and make meaningful side-by-side comparisons between loss functions. Then, using a variety of visualizations, we explore how network architecture affects the loss landscape, and how training parameters affect the shape of minimizers.},
archivePrefix = {arXiv},
arxivId = {1712.09913},
author = {Li, Hao and Xu, Zheng and Taylor, Gavin and Studer, Christoph and Goldstein, Tom},
eprint = {1712.09913},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Li et al. - 2017 - Visualizing the Loss Landscape of Neural Nets.pdf:pdf},
number = {Nips 2018},
title = {{Visualizing the Loss Landscape of Neural Nets}},
url = {http://arxiv.org/abs/1712.09913},
year = {2017}
}
@article{Norman2020,
author = {Norman, Gabriella},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Norman - 2020 - A segmentaion network with a class-agnostic loss function for training on incomplete data.pdf:pdf},
title = {{A segmentaion network with a class-agnostic loss function for training on incomplete data}},
year = {2020}
}
@article{Golkov2016,
abstract = {Numerous scientific fields rely on elaborate but partly suboptimal data processing pipelines. An example is diffusion magnetic resonance imaging (diffusion MRI), a non-invasive microstructure assessment method with a prominent application in neuroimaging. Advanced diffusion models providing accurate microstructural characterization so far have required long acquisition times and thus have been inapplicable for children and adults who are uncooperative, uncomfortable, or unwell. We show that the long scan time requirements are mainly due to disadvantages of classical data processing. We demonstrate how deep learning, a group of algorithms based on recent advances in the field of artificial neural networks, can be applied to reduce diffusion MRI data processing to a single optimized step. This modification allows obtaining scalar measures from advanced models at twelve-fold reduced scan time and detecting abnormalities without using diffusion models. We set a new state of the art by estimating diffusion kurtosis measures from only 12 data points and neurite orientation dispersion and density measures from only 8 data points. This allows unprecedentedly fast and robust protocols facilitating clinical routine and demonstrates how classical data processing can be streamlined by means of deep learning.},
author = {Golkov, Vladimir and Dosovitskiy, Alexey and Sperl, Jonathan I. and Menzel, Marion I. and Czisch, Michael and S{\"{a}}mann, Philipp and Brox, Thomas and Cremers, Daniel},
doi = {10.1109/TMI.2016.2551324},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Golkov et al. - 2016 - q-Space Deep Learning Twelve-Fold Shorter and Model-Free Diffusion MRI Scans.pdf:pdf},
issn = {1558254X},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Artificial neural networks,diffusion kurtosis imaging (DKI),diffusion magnetic resonance imaging (diffusion MR,neurite orientation dispersion and density imaging},
number = {5},
pages = {1344--1351},
pmid = {27071165},
title = {{q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans}},
volume = {35},
year = {2016}
}
@article{Lecun,
abstract = {Multilayer neural networks trained with the back-propagation\nalgorithm constitute the best example of a successful gradient based\nlearning technique. Given an appropriate network architecture,\ngradient-based learning algorithms can be used to synthesize a complex\ndecision surface that can classify high-dimensional patterns, such as\nhandwritten characters, with minimal preprocessing. This paper reviews\nvarious methods applied to handwritten character recognition and\ncompares them on a standard handwritten digit recognition task.\nConvolutional neural networks, which are specifically designed to deal\nwith the variability of 2D shapes, are shown to outperform all other\ntechniques. Real-life document recognition systems are composed of\nmultiple modules including field extraction, segmentation recognition,\nand language modeling. A new learning paradigm, called graph transformer\nnetworks (GTN), allows such multimodule systems to be trained globally\nusing gradient-based methods so as to minimize an overall performance\nmeasure. Two systems for online handwriting recognition are described.\nExperiments demonstrate the advantage of global training, and the\nflexibility of graph transformer networks. A graph transformer network\nfor reading a bank cheque is also described. It uses convolutional\nneural network character recognizers combined with global training\ntechniques to provide record accuracy on business and personal cheques.\nIt is deployed commercially and reads several million cheques per day\n},
archivePrefix = {arXiv},
arxivId = {1102.0183},
author = {LeCun, Yann and Bottou, L{\'{e}}on and Bengio, Yoshua and Haffner, Patrick},
doi = {10.1109/5.726791},
eprint = {1102.0183},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/LeCun et al. - 1998 - Gradient-based learning applied to document recognition.pdf:pdf},
isbn = {0018-9219},
issn = {00189219},
journal = {Proceedings of the IEEE},
keywords = {Convolutional neural networks,Document recognition,Finite state transducers,Gradient-based learning,Graph transformer networks,Machine learning,Neural networks,Optical character recognition (OCR)},
number = {11},
pages = {2278--2323},
pmid = {15823584},
title = {{Gradient-based learning applied to document recognition}},
volume = {86},
year = {1998}
}
@article{Pathak2016,
abstract = {We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders - a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s). When training context encoders, we have experimented with both a standard pixel-wise reconstruction loss, as well as a reconstruction plus an adversarial loss. The latter produces much sharper results because it can better handle multiple modes in the output. We found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures. We quantitatively demonstrate the effectiveness of our learned features for CNN pre-training on classification, detection, and segmentation tasks. Furthermore, context encoders can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods.},
archivePrefix = {arXiv},
arxivId = {1604.07379},
author = {Pathak, Deepak and Krahenbuhl, Philipp and Donahue, Jeff and Darrell, Trevor and Efros, Alexei A.},
doi = {10.1109/CVPR.2016.278},
eprint = {1604.07379},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Pathak et al. - 2016 - Context Encoders Feature Learning by Inpainting.pdf:pdf},
isbn = {9781467388504},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {2536--2544},
title = {{Context Encoders: Feature Learning by Inpainting}},
volume = {2016-Decem},
year = {2016}
}
@article{Campanella2019,
abstract = {The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we present a multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations. We evaluated this framework at scale on a dataset of 44,732 whole slide images from 15,187 patients without any form of data curation. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph nodes resulted in areas under the curve above 0.98 for all cancer types. Its clinical application would allow pathologists to exclude 65–75% of slides while retaining 100% sensitivity. Our results show that this system has the ability to train accurate classification models at unprecedented scale, laying the foundation for the deployment of computational decision support systems in clinical practice.},
author = {Campanella, Gabriele and Hanna, Matthew G. and Geneslaw, Luke and Miraflor, Allen and {Werneck Krauss Silva}, Vitor and Busam, Klaus J. and Brogi, Edi and Reuter, Victor E. and Klimstra, David S. and Fuchs, Thomas J.},
doi = {10.1038/s41591-019-0508-1},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Campanella et al. - 2019 - Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.pdf:pdf},
issn = {1546170X},
journal = {Nature Medicine},
number = {8},
pages = {1301--1309},
pmid = {31308507},
publisher = {Springer US},
title = {{Clinical-grade computational pathology using weakly supervised deep learning on whole slide images}},
url = {http://dx.doi.org/10.1038/s41591-019-0508-1},
volume = {25},
year = {2019}
}
@article{Ahmed1991,
author = {Ahmed, Nasir},
doi = {10.1016/1051-2004(91)90086-Z},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ahmed - 1991 - How I came up with the discrete cosine transform.pdf:pdf},
issn = {10512004},
journal = {Digital Signal Processing},
number = {1},
pages = {4--5},
title = {{How I came up with the discrete cosine transform}},
volume = {1},
year = {1991}
}
@article{Liu2018a,
abstract = {Generative Adversarial Net is a frontier method of generative models for images, audios and videos. In this paper, we focus on conditional image generation and introduce conditional Feature-Matching Generative Adversarial Net to generate images from category labels. By visualizing state-of-art discriminative conditional generative models, we find these networks do not gain clear semantic concepts. Thus we design the loss function in the light of metric learning to measure semantic distance. The proposed model is evaluated on several well-known datasets. It is shown to be of higher perceptual quality and better diversity then existing generative models.},
author = {Liu, Yuzhong and Zhao, Qiyang and Jiang, Cheng},
doi = {10.1109/CISP-BMEI.2017.8302049},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Liu, Zhao, Jiang - 2018 - Conditional image generation using feature-matching GAN.pdf:pdf},
isbn = {9781538619377},
journal = {Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017},
keywords = {Deep Generative Model,Generative Adversarial Net,Image Generation},
number = {October},
pages = {1--5},
title = {{Conditional image generation using feature-matching GAN}},
volume = {2018-Janua},
year = {2018}
}
@article{Goyal2020,
abstract = {At the crossing of the statistical and functional analysis, there exists a relentless quest for an efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising algorithms have been documented in the literature, in spite of which the level of functionality of these algorithms still holds margin to acquire desired level of applicability. Quite often noise affecting the pixels in image is Gaussian in nature and uniformly deters information pixels in image. Based on some specific set of assumptions all methods work optimally, however they tend to create artefacts and remove fine structural details under general conditions. This article focuses on classifying and comparing some of the significant works in the field of denoising.},
author = {Goyal, Bhawna and Dogra, Ayush and Agrawal, Sunil and Sohi, B. S. and Sharma, Apoorav},
doi = {10.1016/j.inffus.2019.09.003},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Goyal et al. - 2020 - Image denoising review From classical to state-of-the-art approaches.pdf:pdf},
issn = {15662535},
journal = {Information Fusion},
keywords = {Denoising,Filters,Hybrid,PSNR,Spatial,Transform},
number = {March 2019},
pages = {220--244},
publisher = {Elsevier B.V.},
title = {{Image denoising review: From classical to state-of-the-art approaches}},
volume = {55},
year = {2020}
}
@article{Meyer1995,
abstract = {Medical imaging data sets are often corrupted by multiplicative inhomogeneities, often referred to as nonuniformities or intensity variations, that hamper the use of quantitative analyses. The authors describe an automatic technique that not only improves the worst situations, such as those encountered with magnetic resonance imaging (MRI) surface coils, but also corrects typical inhomogeneities encountered in routine volume data sets, such as MRI head scans, without generating additional artifact. Because the technique uses only the patient data set, the technique can be applied retrospectively to all data sets, and corrects both patient independent effects, such as rf coil design, and patient dependent effects, such as attenuation of overlying tissue experienced both in high field MRI and X-ray computed tomography (CT). The authors show results for several MRI imaging situations including thorax, head, and breast. Following such corrections, region of interest analyses, volume histograms, and thresholding techniques are more meaningful. The value of such correction algorithms may increase dramatically with increased use of high field strength magnets and associated patient-dependent rf attenuation in overlying tissues.},
author = {Meyer, C. R. and Bland, P. H. and Pipe, J.},
doi = {10.1109/42.370400},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Meyer, Bland, Pipe - 1995 - Retrospective correction of intensity inhomogeneities in MRI.pdf:pdf},
isbn = {0278-0062},
issn = {02780062},
journal = {IEEE Transactions on Medical Imaging},
number = {1},
pages = {36--41},
pmid = {18215808},
title = {{Retrospective correction of intensity inhomogeneities in MRI}},
volume = {14},
year = {1995}
}
@article{Tran2019,
abstract = {Self-supervised (SS) learning is a powerful approach for representation learning using unlabeled data. Recently, it has been applied to Generative Adversarial Networks (GAN) training. Specifically, SS tasks were proposed to address the catastrophic forgetting issue in the GAN discriminator. In this work, we perform an in-depth analysis to understand how SS tasks interact with learning of generator. From the analysis, we identify issues of SS tasks which allow a severely mode-collapsed generator to excel the SS tasks. To address the issues, we propose new SS tasks based on a multi-class minimax game. The competition between our proposed SS tasks in the game encourages the generator to learn the data distribution and generate diverse samples. We provide both theoretical and empirical analysis to support that our proposed SS tasks have better convergence property. We conduct experiments to incorporate our proposed SS tasks into two different GAN baseline models. Our approach establishes state-of-the-art FID scores on CIFAR-10, CIFAR-100, STL-10, CelebA, Imagenet 32 × 32 and Stacked-MNIST datasets, outperforming existing works by considerable margins in some cases. Our unconditional GAN model approaches performance of conditional GAN without using labeled data. Our code: https://github.com/tntrung/msgan},
archivePrefix = {arXiv},
arxivId = {1911.06997},
author = {Tran, Ngoc Trung and Tran, Viet Hung and Nguyen, Ngoc Bao and Yang, Linxiao and Cheung, Ngai Man},
eprint = {1911.06997},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Tran et al. - 2019 - Self-supervised GAN Analysis and improvement with multi-class minimax game.pdf:pdf},
journal = {arXiv},
number = {NeurIPS},
title = {{Self-supervised GAN: Analysis and improvement with multi-class minimax game}},
year = {2019}
}
@article{Kata2018b,
author = {Kata, Kub{\'{i}}nyi and Kata, R{\'{a}}cz},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kata, Kata - 2018 - skandinavisztikai foly{\'{o}}irat 1.pdf:pdf},
title = {skandinavisztikai foly{\'{o}}irat 1.},
year = {2018}
}
@article{Mandt2017,
abstract = {Stochastic Gradient Descent with a constant learning rate (constant SGD) simulates a Markov chain with a stationary distribution. With this perspective, we derive several new results. (1) We show that constant SGD can be used as an approximate Bayesian posterior inference algorithm. Specifically, we show how to adjust the tuning parameters of constant SGD to best match the stationary distribution to a posterior, minimizing the Kullback-Leibler divergence between these two distributions. (2) We demonstrate that constant SGD gives rise to a new variational EM algorithm that optimizes hyperparameters in complex probabilistic models. (3) We also show how to tune SGD with momentum for approximate sampling. (4)We analyze stochastic-gradient MCMC algorithms. For Stochastic-Gradient Langevin Dynamics and Stochastic-Gradient Fisher Scoring, we quantify the approximation errors due to finite learning rates. Finally (5), we use the stochastic process perspective to give a short proof of why Polyak averaging is optimal. Based on this idea, we propose a scalable approximate MCMC algorithm, the Averaged Stochastic Gradient Sampler.},
archivePrefix = {arXiv},
arxivId = {1704.04289},
author = {Mandt, Stephan and {Hof Fman}, Matthew D. and Blei, David M.},
eprint = {1704.04289},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mandt, Hof Fman, Blei - 2017 - Stochastic gradient descent as approximate Bayesian inference.pdf:pdf},
issn = {15337928},
journal = {Journal of Machine Learning Research},
keywords = {Approximate Bayesian inference,Stochastic differential equations,Stochastic gradient MCMC,Stochastic optimization,Variational inference},
pages = {1--35},
title = {{Stochastic gradient descent as approximate Bayesian inference}},
volume = {18},
year = {2017}
}
@article{Atwood2019,
author = {Atwood, Margaret},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Atwood - 2019 - The Age of Lead.pdf:pdf},
isbn = {6103544947},
pages = {5--10},
title = {{The Age of Lead}},
volume = {2},
year = {2019}
}
@article{Chen2020,
author = {Chen, Liangjun and Wu, Zhengwang and Hu, Dan and Wang, Fan and Smith, J Keith and Lin, Weili and Wang, Li and Member, Senior},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Chen et al. - 2020 - AINCnet Adversarial Intensity Nonuniformity Correction Network for Infant Brain MR Images.pdf:pdf},
keywords = {generative adversarial,infant brain,intensity nonuniformity correction,networks},
title = {{AINCnet : Adversarial Intensity Nonuniformity Correction Network for Infant Brain MR Images}},
year = {2020}
}
@article{Quijas2014,
abstract = {JPEG is a commonly used image compression method. While it normally yields very good compression ratios, it also introduces blocking artifacts and quantization noise. In this paper, we present a method to remove noise and blocking effects from JPEG-compressed images. We use machine learning techniques to predict DCT coefficients and pixel values in a compressed image. Results show a decrease in mean square error between our predicted images and the original uncompressed images when compared to the compressed images, as well as a clear reduction of blocking artifacts. {\textcopyright} 2014 IEEE.},
author = {Quijas, Jonathan and Fuentes, Olac},
doi = {10.1109/SSIAI.2014.6806033},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Quijas, Fuentes - 2014 - Removing JPEG blocking artifacts using machine learning.pdf:pdf},
isbn = {9781479940530},
journal = {Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation},
keywords = {Image compression,JPEG,artifact removal,feed-forward neural networks},
pages = {77--80},
title = {{Removing JPEG blocking artifacts using machine learning}},
year = {2014}
}
@article{Jeong,
author = {Jeong, Geunu and Ko, Jingyu and Yoon, Jaeyeon and Won, Jongyun and Kim, Jeewook and Kim, Sunwoo and Song, Seungwoo and Lee, Jingu},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Jeong et al. - Unknown - Feature-level multi-domain learning with standardization for multi-channel data Geunu Jeong, Jingyu Ko, Jaeyeon.pdf:pdf},
number = {c},
pages = {1--3},
title = {{Feature-level multi-domain learning with standardization for multi-channel data Geunu Jeong, Jingyu Ko, Jaeyeon Yoon, Jongyun Won, Jeewook Kim, Sunwoo Kim, Doohee Lee, Seungwoo Song, Jingu Lee}}
}
@article{Hu2020,
abstract = {Computer vision systems have numerous tools to assist in various medical fields, notably in image diagnosis. Computed tomography (CT) is the principal imaging method used to assist in the diagnosis of diseases such as bone fractures, lung cancer, heart disease, and emphysema, among others. Lung cancer is one of the four main causes of death in the world. The lung regions in the CT images are marked manually by a specialist as this initial step is a significant challenge for computer vision techniques. Once defined, the lung regions are segmented for clinical diagnoses. This work proposes an automatic segmentation of the lungs in CT images, using the Convolutional Neural Network (CNN) Mask R-CNN, to specialize the model for lung region mapping, combined with supervised and unsupervised machine learning methods (Bayes, Support Vectors Machine (SVM), K-means and Gaussian Mixture Models (GMMs)). Our approach using Mask R-CNN with the K-means kernel produced the best results for lung segmentation reaching an accuracy of 97.68 ± 3.42% and an average runtime of 11.2 s. We compared our results against other works for validation purposes, and our approach had the highest accuracy and was faster than some state-of-the-art methods.},
author = {Hu, Qinhua and Lu{\'{i}}s, Lu{\'{i}}s Fabr{\'{i}}cio and Holanda, Gabriel Bandeira and Alves, Shara S.A. and Francisco, Francisco H{\'{e}}rcules and Han, Tao and {Rebou{\c{c}}as Filho}, Pedro P.},
doi = {10.1016/j.artmed.2020.101792},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hu et al. - 2020 - An effective approach for CT lung segmentation using mask region-based convolutional neural networks.pdf:pdf},
issn = {18732860},
journal = {Artificial Intelligence in Medicine},
keywords = {Image segmentation lung,Machine learning,Mask R-CNN},
number = {July 2019},
pages = {101792},
pmid = {32143797},
publisher = {Elsevier},
title = {{An effective approach for CT lung segmentation using mask region-based convolutional neural networks}},
url = {https://doi.org/10.1016/j.artmed.2020.101792},
volume = {103},
year = {2020}
}
@article{Oksuz2019,
abstract = {Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single, time-efficient acquisition. Standard MRF reconstructs parametric maps using dictionary matching and requires high computational time. We propose to perform MRF map reconstruction using a recurrent neural network, which exploits the time-dependent information of the MRF signal evolution. We evaluate our method on multiparametric synthetic signals and compare it to existing MRF map reconstruction approaches, including those based on neural networks. Our method achieves state-of-the-art estimates of T1 and T2 values. In addition, the reconstruction time is reduced compared to dictionary-matching based approach.},
archivePrefix = {arXiv},
arxivId = {1812.08155},
author = {Oksuz, Ilkay and Cruz, Gastao and Clough, James and Bustin, Aurelien and Fuin, Nicolo and Botnar, Rene M. and Prieto, Claudia and King, Andrew P. and Schnabel, Julia A.},
doi = {10.1109/ISBI.2019.8759502},
eprint = {1812.08155},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Oksuz et al. - 2019 - Magnetic resonance fingerprinting using recurrent neural networks.pdf:pdf},
isbn = {9781538636411},
issn = {19458452},
journal = {Proceedings - International Symposium on Biomedical Imaging},
keywords = {Gru,Lstm,Magnetic resonance fingerprinting,Parameter mapping,Recurrent neural networks},
pages = {1537--1540},
title = {{Magnetic resonance fingerprinting using recurrent neural networks}},
volume = {2019-April},
year = {2019}
}
@article{Kramer2019,
abstract = {Quantification of magnetic resonance (MR)-based relaxation parameters of tendons and ligaments is challenging due to their very short transverse relaxation times, requiring application of ultra-short echo-time (UTE) imaging sequences. We quantify both T1 and T2⁎ in the quadriceps and patellar tendons of healthy volunteers at a field strength of 3 T and visualize the results based on 3D segmentation by using bivariate histogram analysis. We applied a 3D ultra-short echo-time imaging sequence with either variable repetition times (VTR) or variable flip angles (VFA) for T1 quantification in combination with multi-echo acquisition for extracting T2⁎. The values of both relaxation parameters were subsequently binned for bivariate histogram analysis and corresponding cluster identification, which were subsequently visualized. Based on manually-drawn regions of interest in the tendons on the relaxation parameter maps, T1 and T2⁎ boundaries were selected in the bivariate histogram to segment the quadriceps and patellar tendons and visualize the relaxation times by 3D volumetric rendering. Segmentation of bone marrow, fat, muscle and tendons was successfully performed based on the bivariate histogram analysis. Based on the segmentation results mean T2⁎ relaxation times, over the entire tendon volumes averaged over all subjects, were 1.8 ms ± 0.1 ms and 1.4 ms ± 0.2 ms for the patellar and quadriceps tendons, respectively. The mean T1 value of the patellar tendon, averaged over all subjects, was 527 ms ± 42 ms and 476 ms ± 40 ms for the VFA and VTR acquisitions, respectively. The quadriceps tendon had higher mean T1 values of 662 ms ± 97 ms (VFA method) and 637 ms ± 40 ms (VTR method) compared to the patellar tendon. 3D volumetric visualization of the relaxation times revealed that T1 values are not constant over the volume of both tendons, but vary locally. This work provided additional data to build upon the scarce literature available on relaxation times in the quadriceps and patellar tendons. We were able to segment both tendons and to visualize the relaxation parameter distributions over the entire tendon volumes.},
author = {Kr{\"{a}}mer, M. and Maggioni, M. B. and Brisson, N. M. and Zachow, S. and Teichgr{\"{a}}ber, U. and Duda, G. N. and Reichenbach, J. R.},
doi = {10.1016/j.mri.2019.07.015},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kr{\"{a}}mer et al. - 2019 - T1 and T2⁎ mapping of the human quadriceps and patellar tendons using ultra-short echo-time (UTE) imaging and (2).pdf:pdf},
issn = {18735894},
journal = {Magnetic Resonance Imaging},
keywords = {3D visualization,Patellar ligament,Segmentation,Tendons,UTE},
number = {July},
pages = {29--36},
publisher = {Elsevier},
title = {{T1 and T2⁎ mapping of the human quadriceps and patellar tendons using ultra-short echo-time (UTE) imaging and bivariate relaxation parameter-based volumetric visualization}},
url = {https://doi.org/10.1016/j.mri.2019.07.015},
volume = {63},
year = {2019}
}
@article{Tan2019,
abstract = {Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet.},
archivePrefix = {arXiv},
arxivId = {1905.11946},
author = {Tan, Mingxing and Le, Quoc V.},
eprint = {1905.11946},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Tan, Le - 2019 - EfficientNet Rethinking Model Scaling for Convolutional Neural Networks.pdf:pdf},
title = {{EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}},
url = {http://arxiv.org/abs/1905.11946},
year = {2019}
}
@article{Kata2018c,
author = {Kata, Kub{\'{i}}nyi},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kata - 2018 - skandinavisztikai foly{\'{o}}irat 1.pdf:pdf},
title = {skandinavisztikai foly{\'{o}}irat 1.},
year = {2018}
}
@article{,
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Unknown - 2019 - COMPARISON OF GENERATIVE ADVERSARIAL NETWORKS IN MEDICAL IMAGING APPLICATIONS.pdf:pdf},
title = {{COMPARISON OF GENERATIVE ADVERSARIAL NETWORKS IN MEDICAL IMAGING APPLICATIONS}},
year = {2019}
}
@article{Rend,
author = {T{\'{o}}th, Krisztina},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Rend, Szia - Unknown - t{\'{o}}th krisztina.pdf:pdf},
title = {t{\'{o}}th krisztina}
}
@book{Bishop1996,
abstract = {This new edition of Chalmers's highly regarded and widely read work-translated into fifteen languages-is extensively rewritten and reorganized, reflecting the experience of the author, his colleagues, and correspondents in twenty years of teaching from the previous edition. Significant additions are new chapters on the Bayesian approach to science, the new experimentalism, the nature of scientific laws, and the realism/anti-realism debate. An ideal introduction to scientific method, Chalmers's work is both accessible to beginners and a valuable resource for advanced students and scholars.},
author = {Bishop, Michael},
booktitle = {Teaching Philosophy},
doi = {10.5840/teachphil199619224},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Bishop - 1996 - What is this thing called Science.pdf:pdf},
isbn = {0872204529},
number = {2},
pages = {204--206},
title = {{What is this thing called Science?}},
volume = {19},
year = {1996}
}
@article{Chen2014,
abstract = {Eu<inf>2</inf>O<inf>3</inf> doped Y<inf>3</inf>Al<inf>5</inf>O<inf>12</inf> (YAG) crystals have been grown using a floating zone technique and evaluated thermal stability and annealing behavior of PL for a fluorescence thermo-sensor application. Color of the crystals grown varies from deep purple to colorless with O<inf>2</inf> concentration of the growth atmosphere and annealing in air. Photoluminescence (PL) peaking at $\lambda$ = 590, 607, 624, 647 and 709 nm due to Eu<sup>3+</sup> ions are observed from the crystals under UV excitation. Anomalous temperature dependence of PL intensity, which is observed in as-grown crystals, is improved greatly by annealing through the heat cycle. From annealing behavior of optical absorption spectra, residual Eu<sup>2+</sup> ions are suggested to be responsible for the de-coloration and the improvement of anomalous temperature dependence of Eu doped YAG crystals.},
archivePrefix = {arXiv},
arxivId = {0521865719 9780521865715},
author = {Kiyokawa, Yuka and Inoue, Hirofumi and Katsumata, Toru and Komuro, Shuji and Aizawa, Hiroaki},
doi = {10.1016/j.optmat.2014.07.012},
eprint = {0521865719 9780521865715},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kiyokawa et al. - 2014 - Thermal stability and annealing behavior of photoluminescence from Eu doped YAG.pdf:pdf},
isbn = {9781595936486},
issn = {09253467},
journal = {Optical Materials},
keywords = {Annealing,Eu doped YAG,Fluorescence thermometer,Optical absorption spectrum,Photoluminescence spectrum},
number = {C},
pages = {493--497},
pmid = {11807537},
title = {{Real-time Edge-Aware Image Processing with the Bilateral Grid}},
url = {http://portal.acm.org/citation.cfm?doid=1275808.1276506},
volume = {37},
year = {2014}
}
@article{Karras2018,
abstract = {We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.},
archivePrefix = {arXiv},
arxivId = {1812.04948},
author = {Karras, Tero and Laine, Samuli and Aila, Timo},
eprint = {1812.04948},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Karras, Laine, Aila - 2018 - A Style-Based Generator Architecture for Generative Adversarial Networks.pdf:pdf},
title = {{A Style-Based Generator Architecture for Generative Adversarial Networks}},
url = {http://arxiv.org/abs/1812.04948},
year = {2018}
}
@article{Radford2015,
abstract = {The exoplanet KOI-142b (Kepler-88b) shows transit timing variations (TTVs) with a semi-amplitude of $\sim$12 h, which earned it the nickname "king of transit variations". Only the transit of planet b was detected in the Kepler data with an orbital period of $\sim$10.92 days and a radius of $\sim$0.36 R Jup . The TTVs together with the transit duration variations of KOI-142b were analysed recently, finding a unique solution for a companion-perturbing planet. An outer non-transiting companion was predicted, KOI-142c, with a mass of 0.626 ± 0.03 M Jup and a period of 22.3397 -0.0018 +0.0021 days, which is close to the 2:1 mean-motion resonance with the inner transiting planet. We report an independent confirmation of KOI-142c using radial velocity observations with the SOPHIE spectrograph at the Observatoire de Haute-Provence. We derive an orbital period of 22.10 ± 0.25 days and a minimum planetary mass of 0.76 0.16 +0.32 M Jup , both in good agreement with the predictions by previous transit timing analysis. Therefore, this is the first radial velocity confirmation of a non-transiting planet discovered with TTVs, providing an independent validation of the TTVs technique. {\textcopyright} ESO, 2013.},
archivePrefix = {arXiv},
arxivId = {1511.06434},
author = {Radford, A. and Metz, L. and Chintala, S.},
doi = {10.1051/0004-6361/201527329},
eprint = {1511.06434},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Radford, Metz, Chintala - 2015 - UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS.pdf:pdf},
isbn = {2004012439},
issn = {0004-6361},
journal = {Astronomy & Astrophysics},
pages = {A64},
pmid = {23459267},
title = {{UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS}},
url = {http://arxiv.org/abs/1511.06434},
volume = {587},
year = {2015}
}
@article{Larsen2014,
abstract = {Although N3 is perhaps the most widely used method for MRI bias field correction, its underlying mechanism is in fact not well understood. Specifically, the method relies on a relatively heuristic recipe of alternating iterative steps that does not optimize any particular objec- tive function. In this paper we explain the successful bias field correction properties of N3 by showing that it implicitly uses the same generative models and computational strategies as expectation maximization (EM) based bias field correction methods.We demonstrate experimentally that purely EM-based methods are capable of producing bias field correction results comparable to those of N3 in less computation time.},
author = {Larsen, Christian Thode and {Eugenio Iglesias}, J. and {Van Leemput}, Koen},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Larsen, Eugenio Iglesias, Van Leemput - 2014 - N3 bias field correction explained as a Bayesian modeling method.pdf:pdf},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {1--12},
title = {{N3 bias field correction explained as a Bayesian modeling method}},
volume = {8677},
year = {2014}
}
@article{Ashburner2015,
abstract = {{\textcopyright} 2015 American Institute of Chemical Engineers. A novel mini induction heating fluidized bed reactor (IHFBR) is introduced which was developed to carry out screening tests of high temperature reactions up to 1500°C particularly for solid feedstocks. Despite conventional mini reactors, this reactor mimics real scenario of solid feeding in industrial reactors: cold feedstock is injected within 1 s from a lift tube, then particles reach reaction temperature in less than 5 s in a reaction zone. The lift tube (9.5 cm diameter) is also the gas distributor of the fluidized bed (2.5 cm diameter) so that the bed is completely fluidized with uniform gas distribution. Beside facilities to perform tests in a fluidized bed, another important feature of this reactor is prediction of the defluidization state in the bed. Not only reproducible data are generated, but also many tests can be conveniently carried out, that is, one test per hour.},
author = {Ashburner, John},
doi = {10.1002/aic.14749},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ashburner - 2015 - SPM12 Manual.pdf:pdf},
issn = {15475905},
journal = {AIChE Journal},
keywords = {Coal combustion,Defluidization,Fluidization,Induction heating,Lift tube},
number = {5},
pages = {1507--1523},
title = {{SPM12 Manual}},
volume = {61},
year = {2015}
}
@article{Yu2019,
abstract = {Due to the irregular motion, similar appearance and diverse shape, accurate segmentation of kidney tumor in CT images is a difficult and challenging task. To this end, we present a novel automatic segmentation method, termed as Crossbar-Net, with the goal of accurate segmenting the kidney tumors. Firstly, considering that the traditional learning-based segmentation methods normally employ either whole images or squared patches as the training samples, we innovatively sample the orthogonal non-squared patches (namely crossbar patches), to fully cover the whole kidney tumors in either horizontal or vertical directions. These sampled crossbar patches could not only represent the detailed local information of kidney tumor as the traditional patches, but also describe the global appearance from either horizontal or vertical direction using contextual information. Secondly, with the obtained crossbar patches, we trained a convolutional neural network with two sub-models (i.e., horizontal sub-model and vertical sub-model) in a cascaded manner, to integrate the segmentation results from two directions (i.e., horizontal and vertical). This cascaded training strategy could effectively guarantee the consistency between sub-models, by feeding each other with the most difficult samples, for a better segmentation. In the experiment, we evaluate our method on a real CT kidney tumor dataset, collected from 94 different patients including 3,500 images. Compared with the state-of-the-art segmentation methods, the results demonstrate the superior results of our method on dice ratio score, true positive fraction, centroid distance and Hausdorff distance. Moreover, we have extended our crossbar-net to a different task: cardiac segmentation, showing the promising results for the better generalization.},
archivePrefix = {arXiv},
arxivId = {arXiv:1804.10484v3},
author = {Yu, Qian and Shi, Yinghuan and Sun, Jinquan and Gao, Yang and Zhu, Jianbing and Dai, Yakang},
doi = {10.1109/TIP.2019.2905537},
eprint = {arXiv:1804.10484v3},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Yu et al. - 2019 - Crossbar-Net A Novel Convolutional Neural Network for Kidney Tumor Segmentation in CT Images.pdf:pdf},
issn = {10577149},
journal = {IEEE Transactions on Image Processing},
keywords = {CT images,Deep convolutional neural network,crossbar-net,image segmentation,kidney tumors},
number = {8},
pages = {4060--4074},
title = {{Crossbar-Net: A Novel Convolutional Neural Network for Kidney Tumor Segmentation in CT Images}},
volume = {28},
year = {2019}
}
@article{Ibbotson,
author = {Ibbotson, Roger G.},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ibbotson - Unknown - The true impact of Asset Allocation on Returns.pdf:pdf},
title = {{The true impact of Asset Allocation on Returns}}
}
@article{Jaderberg2015,
abstract = {Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner. In this work we introduce a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network. This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimisation process. We show that the use of spatial transformers results in models which learn invariance to translation, scale, rotation and more generic warping, resulting in state-of-the-art performance on several benchmarks, and for a number of classes of transformations.},
archivePrefix = {arXiv},
arxivId = {1506.02025},
author = {Jaderberg, Max and Simonyan, Karen and Zisserman, Andrew and Kavukcuoglu, Koray},
doi = {10.1038/nbt.3343},
eprint = {1506.02025},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Jaderberg et al. - 2015 - Spatial Transformer Networks Max.pdf:pdf},
isbn = {9781627480031},
issn = {1087-0156},
journal = {Nature Biotechnology},
number = {12},
pages = {1242--1249},
pmid = {26571099},
title = {{Spatial Transformer Networks Max}},
url = {http://arxiv.org/abs/1506.02025},
volume = {33},
year = {2015}
}
@article{Cubuk2018,
abstract = {Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars.},
archivePrefix = {arXiv},
arxivId = {1805.09501},
author = {Cubuk, Ekin D. and Zoph, Barret and Mane, Dandelion and Vasudevan, Vijay and Le, Quoc V.},
eprint = {1805.09501},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Cubuk et al. - 2018 - AutoAugment Learning Augmentation Policies from Data.pdf:pdf},
number = {Section 3},
title = {{AutoAugment: Learning Augmentation Policies from Data}},
url = {http://arxiv.org/abs/1805.09501},
year = {2018}
}
@article{Shinohara2014,
author = {Shinohara, Russell T and Sweeney, Elizabeth M and Goldsmith, Jeff and Shiee, Navid and Mateen, Farrah J and Calabresi, Peter A and Jarso, Samson and Pham, Dzung L and Reich, Daniel S and Crainiceanu, Ciprian M},
doi = {10.1016/j.nicl.2014.08.008},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Shinohara et al. - 2014 - Statistical normalization techniques for magnetic resonance imaging.pdf:pdf},
journal = {Ynicl},
keywords = {Image analysis,Magnetic resonance imaging,Normalization,Statistics},
pages = {9--19},
title = {{Statistical normalization techniques for magnetic resonance imaging}},
volume = {6},
year = {2014}
}
@article{Saxena2020,
abstract = {Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.},
archivePrefix = {arXiv},
arxivId = {2005.00065},
author = {Saxena, Divya and Cao, Jiannong},
eprint = {2005.00065},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Saxena, Cao - 2020 - Generative Adversarial Networks (GANs) Challenges, Solutions, and Future Directions.pdf:pdf},
journal = {arXiv},
keywords = {Deep Generative models,Deep learning,GANs,GANs applications,GANs solution,Generative Adversarial Networks,Image generation},
title = {{Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions}},
year = {2020}
}
@article{Messud2018a,
author = {Jelenkor},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Messud et al. - 2018 - Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat.pdf:pdf},
title = {{Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat 1901}},
year = {2019}
}
@article{Loshchilov2017,
abstract = {L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it "weight decay" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at https://github.com/loshchil/AdamW-and-SGDW},
archivePrefix = {arXiv},
arxivId = {1711.05101},
author = {Loshchilov, Ilya and Hutter, Frank},
doi = {10.1111/cbdd.12322},
eprint = {1711.05101},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Loshchilov, Hutter - 2017 - Decoupled Weight Decay Regularization.pdf:pdf},
isbn = {1711.05101v2},
issn = {10495258},
pmid = {22365971},
title = {{Decoupled Weight Decay Regularization}},
url = {http://arxiv.org/abs/1711.05101},
year = {2017}
}
@article{Jansen2015,
author = {Jansen, M.J.A.},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Jansen - 2015 - Evaluation of intensity normalization methods for MR images.pdf:pdf},
number = {July},
pages = {3--30},
title = {{Evaluation of intensity normalization methods for MR images}},
year = {2015}
}
@article{Taborosi2019,
author = {T{\'{a}}borosi, A. R.},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/T{\'{a}}borosi - 2019 - FELSŐV{\'{E}}GTAGI FUNKCI{\'{O}} JAV{\'{I}}T{\'{A}}SA C{\'{E}}LJ{\'{A}}B{\'{O}}L V{\'{E}}GZETT ROT{\'{A}}CI{\'{O}}S OSTEOTOMIA FIZIOTER{\'{A}}PI{\'{A}}JA.pdf:pdf},
title = {{FELSŐV{\'{E}}GTAGI FUNKCI{\'{O}} JAV{\'{I}}T{\'{A}}SA C{\'{E}}LJ{\'{A}}B{\'{O}}L V{\'{E}}GZETT ROT{\'{A}}CI{\'{O}}S OSTEOTOMIA FIZIOTER{\'{A}}PI{\'{A}}JA}},
year = {2019}
}
@article{He2019,
abstract = {<p> Matter evolved under the influence of gravity from minuscule density fluctuations. Nonperturbative structure formed hierarchically over all scales and developed non-Gaussian features in the Universe, known as the cosmic web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and use a large ensemble of computer simulations to compare with the observed data to extract the full information of our own Universe. However, to evolve billions of particles over billions of years, even with the simplest physics, is a daunting task. We build a deep neural network, the Deep Density Displacement Model ( <inline-formula> <math overflow="scroll"> <msup> <mrow> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msup> <mi mathvariant="normal">M</mi> </math> </inline-formula> ), which learns from a set of prerun numerical simulations, to predict the nonlinear large-scale structure of the Universe with the Zel'dovich Approximation (ZA), an analytical approximation based on perturbation theory, as the input. Our extensive analysis demonstrates that <inline-formula> <math overflow="scroll"> <msup> <mrow> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msup> <mi mathvariant="normal">M</mi> </math> </inline-formula> outperforms the second-order perturbation theory (2LPT), the commonly used fast-approximate simulation method, in predicting cosmic structure in the nonlinear regime. We also show that <inline-formula> <math overflow="scroll"> <msup> <mrow> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msup> <mi mathvariant="normal">M</mi> </math> </inline-formula> is able to accurately extrapolate far beyond its training data and predict structure formation for significantly different cosmological parameters. Our study proves that deep learning is a practical and accurate alternative to approximate 3D simulations of the gravitational structure formation of the Universe. </p>},
author = {He, Siyu and Li, Yin and Feng, Yu and Ho, Shirley and Ravanbakhsh, Siamak and Chen, Wei and P{\'{o}}czos, Barnab{\'{a}}s},
doi = {10.1073/pnas.1821458116},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/He et al. - 2019 - Learning to predict the cosmological structure formation.pdf:pdf},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences},
number = {28},
pages = {201821458},
title = {{Learning to predict the cosmological structure formation}},
url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1821458116},
volume = {116},
year = {2019}
}
@article{Tustison2010,
author = {Tustison, Nicholas J. and Avants, Brian B. and Cook, Philip A. and Zheng, Yuanjie and Egan, Alexander and Yushkevich, Paul A. and Gee, James C.},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Tustison et al. - 2010 - N4ITK Improved N3 bias correction.pdf:pdf},
journal = {IEEE Transactions on Medical Imaging},
number = {6},
pages = {1310--1320},
title = {{N4ITK: Improved N3 bias correction}},
volume = {29},
year = {2010}
}
@misc{Osman,
author = {Osman, Jena},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Osman - Unknown - The Franklin Party.pdf.pdf:pdf},
title = {{The Franklin Party.pdf}}
}
@article{Reisert2017,
abstract = {Diffusion-sensitized magnetic resonance imaging probes the cellular structure of the human brain, but the primary microstructural information gets lost in averaging over higher-level, mesoscopic tissue organization such as different orientations of neuronal fibers. While such averaging is inevitable due to the limited imaging resolution, we propose a method for disentangling the microscopic cell properties from the effects of mesoscopic structure. We further avoid the classical fitting paradigm and use supervised machine learning in terms of a Bayesian estimator to estimate the microstructural properties. The method finds detectable parameters of a given microstructural model and calculates them within seconds, which makes it suitable for a broad range of neuroscientific applications.},
author = {Reisert, Marco and Kellner, Elias and Dhital, Bibek and Hennig, J{\"{u}}rgen and Kiselev, Valerij G.},
doi = {10.1016/j.neuroimage.2016.09.058},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Reisert et al. - 2017 - Disentangling micro from mesostructure by diffusion MRI A Bayesian approach.pdf:pdf},
issn = {10959572},
journal = {NeuroImage},
keywords = {Axonal density,Diffusion MRI,Microstructural parameters,Microstructure imaging,Multi-shell dMRI,White matter},
number = {October 2016},
pages = {964--975},
pmid = {27746388},
title = {{Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach}},
volume = {147},
year = {2017}
}
@article{Dong,
archivePrefix = {arXiv},
arxivId = {arXiv:1809.00219v2},
author = {Dong, Chao and Loy, Chen Change and Qiao, Yu and Tang, Xiaoou},
eprint = {arXiv:1809.00219v2},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Dong et al. - Unknown - ESRGAN Enhanced Super-Resolution.pdf:pdf},
title = {{ESRGAN : Enhanced Super-Resolution}}
}
@article{Holden2019,
abstract = {Data-driven methods for physical simulation are an attractive option for interactive applications due to their ability to trade precomputation and memory footprint in exchange for improved runtime performance. Yet, existing data-driven methods fall short of the extreme memory and performance constraints imposed by modern interactive applications like AAA games and virtual reality. Here, performance budgets for physics simulation range from tens to hundreds of micro-seconds per frame, per object. We present a data-driven physical simulation method that meets these constraints. Our method combines subspace simulation techniques with machine learning which, when coupled, enables a very efficient subspace-only physics simulation that supports interactions with external objects - a longstanding challenge for existing subspace techniques. We also present an interpretation of our method as a special case of subspace Verlet integration, where we apply machine learning to efficiently approximate the physical forces of the system directly in the subspace. We propose several practical solutions required to make effective use of such a model, including a novel training methodology required for prediction stability, and a GPU-friendly subspace decompression algorithm to accelerate rendering.},
author = {Holden, Daniel and Duong, Bang Chi and Datta, Sayantan and Nowrouzezahrai, Derek},
doi = {10.1145/3309486.3340245},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Holden et al. - 2019 - Subspace neural physics Fast data-driven interactive simulation.pdf:pdf},
isbn = {9781450366779},
journal = {Proceedings - SCA 2019: ACM SIGGRAPH / Eurographics Symposium on Computer Animation},
keywords = {Cloth simulation,Collision detection,Data-driven simulation,Machine learning,Model reduction,Neural networks},
title = {{Subspace neural physics: Fast data-driven interactive simulation}},
year = {2019}
}
@article{Bernal2018,
abstract = {In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation. These methods have also been utilised in medical image analysis domain for lesion segmentation, anatomical segmentation and classification. We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works. The aim of this study is three-fold. Our primary goal is to report how different CNN architectures have evolved, discuss state-of-the-art strategies, condense their results obtained using public datasets and examine their pros and cons. Second, this paper is intended to be a detailed reference of the research activity in deep CNN for brain MRI analysis. Finally, we present a perspective on the future of CNNs in which we hint some of the research directions in subsequent years.},
archivePrefix = {arXiv},
arxivId = {arXiv:1712.03747v3},
author = {Bernal, Jose and Kushibar, Kaisar and Asfaw, Daniel S. and Valverde, Sergi and Oliver, Arnau and Mart{\'{i}}, Robert and Llad{\'{o}}, Xavier},
doi = {10.1016/j.artmed.2018.08.008},
eprint = {arXiv:1712.03747v3},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Bernal et al. - 2018 - Deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review.pdf:pdf},
isbn = {978-1-4673-6964-0},
issn = {18732860},
journal = {Artificial Intelligence in Medicine},
keywords = {Brain MRI,Deep convolutional neural network,Review,Segmentation},
pmid = {21728107},
title = {{Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review}},
year = {2018}
}
@article{He2015,
author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/He et al. - 2015 - Deep Residual Learning for Image Recognition.pdf:pdf},
title = {{Deep Residual Learning for Image Recognition}},
year = {2015}
}
@article{Schuld2015,
abstract = {Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.},
archivePrefix = {arXiv},
arxivId = {1409.3097},
author = {Schuld, Maria and Sinayskiy, Ilya and Petruccione, Francesco},
doi = {10.1080/00107514.2014.964942},
eprint = {1409.3097},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Schuld, Sinayskiy, Petruccione - 2015 - An introduction to quantum machine learning.pdf:pdf},
issn = {13665812},
journal = {Contemporary Physics},
keywords = {artificial intelligence,machine learning,quantum computing,quantum machine learning},
number = {2},
pages = {172--185},
publisher = {Taylor & Francis},
title = {{An introduction to quantum machine learning}},
url = {http://dx.doi.org/10.1080/00107514.2014.964942},
volume = {56},
year = {2015}
}
@article{Hwang2019,
abstract = {We propose a new deep learning–based approach to provide more accurate whole-body PET/MRI attenuation correction than is possible with the Dixon-based 4-segment method. We use activity and attenuation maps estimated using the maximum-likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a convolutional neural network (CNN) to learn a CT-derived attenuation map. Methods: The whole-body 18F-FDG PET/CT scan data of 100 cancer patients (38 men and 62 women; age, 57.3 ± 14.1 y) were retrospectively used for training and testing the CNN. A modified U-net was trained to predict a CT-derived $\mu$-map ($\mu$-CT) from the MLAA-generated activity distribution (l-MLAA) and $\mu$-map ($\mu$-MLAA). We used 1.3 million patches derived from 60 patients' data for training the CNN, data of 20 others were used as a validation set to prevent overfitting, and the data of the other 20 were used as a test set for the CNN performance analysis. The attenuation maps generated using the proposed method ($\mu$-CNN), $\mu$-MLAA, and 4-segment method ($\mu$-segment) were compared with the $\mu$-CT, a ground truth. We also compared the voxelwise correlation between the activity images reconstructed using ordered-subset expectation maximization with the $\mu$-maps, and the SUVs of primary and metastatic bone lesions obtained by drawing regions of interest on the activity images. Results: The CNN generates less noisy attenuation maps and achieves better bone identification than MLAA. The average Dice similarity coefficient for bone regions between $\mu$-CNN and $\mu$-CT was 0.77, which was significantly higher than that between $\mu$-MLAA and $\mu$-CT (0.36). Also, the CNN result showed the best pixel-by-pixel correlation with the CT-based results and remarkably reduced differences in activity maps in comparison to CT-based attenuation correction. Conclusion: The proposed deep neural network produced a more reliable attenuation map for 511-keV photons than the 4-segment method currently used in whole-body PET/MRI studies.},
author = {Hwang, Donghwi and Kang, Seung Kwan and Kim, Kyeong Yun and Seo, Seongho and Paeng, Jin Chul and Lee, Dong Soo and Lee, Jae Sung},
doi = {10.2967/jnumed.118.219493},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hwang et al. - 2019 - Generation of PET attenuation map for whole-body time-of-flight 18F-FDG PETMRI using a deep neural network trai(2).pdf:pdf},
issn = {2159662X},
journal = {Journal of Nuclear Medicine},
keywords = {Attenuation correction,Deep learning,PET/MRI,Simultaneous reconstruction},
number = {8},
pages = {1183--1189},
title = {{Generation of PET attenuation map for whole-body time-of-flight 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps}},
volume = {60},
year = {2019}
}
@article{Christov-Moore2020,
abstract = {Recent task fMRI studies suggest that individual differences in trait empathy and empathic concern are mediated by patterns of connectivity between self-other resonance and top-down control networks that are stable across task demands. An untested implication of this hypothesis is that these stable patterns of connectivity should be visible even in the absence of empathy tasks. Using machine learning, we demonstrate that patterns of resting state fMRI connectivity (i.e. the degree of synchronous BOLD activity across multiple cortical areas in the absence of explicit task demands) of resonance and control networks predict trait empathic concern (n = 58). Empathic concern was also predicted by connectivity patterns within the somatomotor network. These findings further support the role of resonance-control network interactions and of somatomotor function in our vicariously driven concern for others. Furthermore, a practical implication of these results is that it is possible to assess empathic predispositions in individuals without needing to perform conventional empathy assessments.},
author = {Christov-Moore, Leonardo and Reggente, Nicco and Douglas, Pamela K. and Feusner, Jamie D. and Iacoboni, Marco},
doi = {10.3389/fnint.2020.00003},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Christov-Moore et al. - 2020 - Predicting Empathy From Resting State Brain Connectivity A Multivariate Approach(2).pdf:pdf},
issn = {16625145},
journal = {Frontiers in Integrative Neuroscience},
keywords = {connectivity,empathic concern,empathy,experience sharing,fMRI,machine learning,mirroring,resting state},
number = {February},
pages = {1--11},
title = {{Predicting Empathy From Resting State Brain Connectivity: A Multivariate Approach}},
volume = {14},
year = {2020}
}
@article{Fetty,
author = {Fetty, Lukas and Bylund, Mikael and Kuess, Peter and Nyholm, Tufve and Georg, Dietmar and Tommy, L},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Fetty et al. - Unknown - StyleGAN Latent Space Manipulation for Medical Image Synthesis.pdf:pdf},
number = {X},
pages = {1--8},
title = {{StyleGAN Latent Space Manipulation for Medical Image Synthesis}},
volume = {XX}
}
@article{Wang2019,
abstract = {Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. In this survey, we aim to give a survey on recent advances of image super-resolution techniques using deep learning approaches in a systematic way. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future directions and open issues which should be further addressed by the community in the future.},
archivePrefix = {arXiv},
arxivId = {1902.06068},
author = {Wang, Zhihao and Chen, Jian and Hoi, Steven C.H.},
doi = {10.1109/tpami.2020.2982166},
eprint = {1902.06068},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wang, Chen, Hoi - 2019 - Deep learning for image super-resolution A survey.pdf:pdf},
issn = {0162-8828},
journal = {arXiv},
keywords = {Convolutional neural networks (CNN),Deep learning,Generative adversarial nets (GAN),Image super-resolution},
number = {c},
pages = {1--22},
publisher = {IEEE},
title = {{Deep learning for image super-resolution: A survey}},
volume = {8828},
year = {2019}
}
@article{Odena2016,
abstract = {We extend Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels. We train a generative model G and a discriminator D on a dataset with inputs belonging to one of N classes. At training time, D is made to predict which of N+1 classes the input belongs to, where an extra class is added to correspond to the outputs of G. We show that this method can be used to create a more data-efficient classifier and that it allows for generating higher quality samples than a regular GAN.},
archivePrefix = {arXiv},
arxivId = {1606.01583},
author = {Odena, Augustus},
eprint = {1606.01583},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Odena - 2016 - Semi-Supervised Learning with Generative Adversarial Networks.pdf:pdf},
pages = {1--3},
title = {{Semi-Supervised Learning with Generative Adversarial Networks}},
url = {http://arxiv.org/abs/1606.01583},
year = {2016}
}
@article{Kingma2014,
abstract = {How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.},
archivePrefix = {arXiv},
arxivId = {1312.6114},
author = {Kingma, Diederik P. and Welling, Max},
eprint = {1312.6114},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kingma, Welling - 2014 - Auto-encoding variational bayes.pdf:pdf},
journal = {2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings},
number = {Ml},
pages = {1--14},
title = {{Auto-encoding variational bayes}},
year = {2014}
}
@article{Ding2020,
abstract = {The recent outbreak of novel coronavirus (SARS-CoV-2) causing COVID-19 disease spreads rapidly in the world. Rapid and early detection of SARS-CoV-2 facilitates early intervention and prevents the disease spread. Here, we present an All-In-One Dual CRISPR-Cas12a (AIOD-CRISPR) assay for one-pot, ultrasensitive, and visual SARS-CoV-2 detection. By targeting SARS-CoV-2's nucleoprotein gene, two CRISPR RNAs without protospacer adjacent motif (PAM) site limitation are introduced to develop the AIOD-CRISPR assay and detect the nucleic acids with a sensitivity of few copies. We validate the assay by using COVID-19 clinical swab samples and obtain consistent results with RT-PCR assay. Furthermore, a low-cost hand warmer ($\sim$$0.3) is used as an incubator of the AIOD-CRISPR assay to detect clinical samples within 20 min, enabling an instrument-free, visual SARS-CoV-2 detection at the point of care. Thus, our method has the significant potential to provide a rapid, sensitive, one-pot point-of-care assay for SARS-CoV-2.},
author = {Ding, Xiong and Yin, Kun and Li, Ziyue and Lalla, Rajesh V. and Ballesteros, Enrique and Sfeir, Maroun M. and Liu, Changchun},
doi = {10.1038/s41467-020-18575-6},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ding et al. - 2020 - Ultrasensitive and visual detection of SARS-CoV-2 using all-in-one dual CRISPR-Cas12a assay.pdf:pdf},
issn = {20411723},
journal = {Nature Communications},
number = {1},
pages = {1--10},
pmid = {32948757},
title = {{Ultrasensitive and visual detection of SARS-CoV-2 using all-in-one dual CRISPR-Cas12a assay}},
volume = {11},
year = {2020}
}
@article{Neyshabur2017,
abstract = {With a goal of understanding what drives generalization in deep networks, we consider several recently suggested explanations, including norm-based control, sharpness and robustness. We study how these measures can ensure generalization, highlighting the importance of scale normalization, and making a connection between sharpness and PAC-Bayes theory. We then investigate how well the measures explain different observed phenomena.},
archivePrefix = {arXiv},
arxivId = {1706.08947},
author = {Neyshabur, Behnam and Bhojanapalli, Srinadh and McAllester, David and Srebro, Nathan},
eprint = {1706.08947},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Neyshabur et al. - 2017 - Exploring generalization in deep learning.pdf:pdf},
issn = {10495258},
journal = {Advances in Neural Information Processing Systems},
pages = {5948--5957},
title = {{Exploring generalization in deep learning}},
volume = {2017-Decem},
year = {2017}
}
@article{Factor2002,
author = {Factor, Impact},
doi = {10.4135/9781529714913.n5},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Factor - 2002 - Writing in science.pdf:pdf},
issn = {00219584},
journal = {Journal of Chemical Education},
number = {SUPPL.},
pages = {36},
title = {{Writing in science}},
volume = {79},
year = {2002}
}
@article{E.E.1850,
author = {{E. E.}, Barclay},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/E. E. - 1850 - The Extraordinary and all-absorbing journal of WM. N. Seldon.pdf:pdf},
journal = {Work},
number = {06},
pages = {1--59},
title = {{The Extraordinary and all-absorbing journal of WM. N. Seldon}},
year = {1850}
}
@article{Madabhushi2005,
abstract = {Image intensity standardization is a postprocessing method designed for correcting acquisition-to-acquisition signal intensity variations (nonstandardness) inherent in magnetic resonance (MR) images. Inhomogeneity correction is a process used to suppress the low frequency background nonuniformities (inhomogeneities) of the image domain that exist in MR images. Both these procedures have important implications in MR image analysis. The effects of these postprocessing operations on improvement of image quality in isolation has been well documented. However, the combined effects of these two processes on MR images and how the processes influence each other have not been studied thus far. In this paper, we evaluate the effect of inhomogeneity correction followed by standardization and vice-versa on MR images in order to determine the best sequence to follow for enhancing image quality. We conducted experiments on several clinical and phantom data sets (nearly 4000 three-dimensional MR images were analyzed) corresponding to four different MRI protocols. Different levels of artificial nonstandardness, and different models and levels of artificial background inhomogeneity were used in these experiments. Our results indicate that improved standardization can be achieved by preceding it with inhomogeneity correction. There is no statistically significant difference in image quality obtained between the results of standardization followed by correction and that of correction followed by standardization from the perspective of inhomogeneity correction. The correction operation is found to bias the effect of standardization. We demonstrate this bias both qualitatively and quantitatively by using two different methods of inhomogeneity correction. We also show that this bias in standardization is independent of the specific inhomogeneity correction method used. The effect of this bias due to correction was also seen in magnetization transfer ratio (MTR) images, which are naturall- - y endowed with the standardness property. Standardization, on the other hand, does not seem to influence the correction operation. It is also found that longer sequences of repeated correction and standardization operations do not considerably improve image quality. These results were found to hold for the clinical and the phantom data sets, for different MRI protocols, for different levels of artificial nonstandardness, for different models and levels of artificial inhomogeneity, for different correction methods, and for images that were endowed with inherent standardness as well as for those that were standardized by using the intensity standardization method. Overall, we conclude that inhomogeneity correction followed by intensity standardization is the best sequence to follow from the perspective of both image quality and computational efficiency.},
author = {Madabhushi, Anant and Udupa, Jayaram K.},
doi = {10.1109/TMI.2004.843256},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Madabhushi, Udupa - 2005 - Interplay between intensity standardization and inhomogeneity correction in MR image processing.pdf:pdf},
issn = {02780062},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Image analysis,Image processing,Inhomogeneity correction,MRI,Standardization},
number = {5},
pages = {561--576},
title = {{Interplay between intensity standardization and inhomogeneity correction in MR image processing}},
volume = {24},
year = {2005}
}
@article{He2017,
abstract = {We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code has been made available at: https://github.com/facebookresearch/Detectron},
archivePrefix = {arXiv},
arxivId = {arXiv:1703.06870v3},
author = {He, Kaiming and Gkioxari, Georgia and Dollar, Piotr and Girshick, Ross},
doi = {10.1109/ICCV.2017.322},
eprint = {arXiv:1703.06870v3},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/He et al. - 2017 - Mask R-CNN.pdf:pdf},
isbn = {9781538610329},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {2980--2988},
title = {{Mask R-CNN}},
volume = {2017-Octob},
year = {2017}
}
@article{Stollberger1996,
abstract = {The authors describe a method for accurate in vhro multislice imaging of the active component of the 6, field which is based on a previously proposed method, which uses the signal in- tensity ratio of two images measured with different excitation angles, and a repetition time TR z 5 T,. The new method essentially reduces repetition and scan time by means of an additional compensating pulse. The suppression of Ti effects by this pulse are verified with simulations and measurements. Further investigations concerned the influence of slice selec- tive excitation and magnetization transfer in multislice imag- ing to the 6, field determination. The stability and accuracy of the presented method is shown by several phantom and in vhro measurements. With the described method the active 6, field can be determined in vko in 23 cross-sections in less than 6 min.},
author = {Stollberger, Rudolf and Wach, Paul},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Stollberger, Wach - 1996 - Imaging of the active {B}_1 field {em in vivo}.pdf:pdf},
issn = {1522-2594},
journal = {Magn. Reson. Med.},
keywords = {RF field,S1 mapping,in vivo S1 distribution},
number = {2},
pages = {246--251},
title = {{Imaging of the active {B}_1 field {\em in vivo}}},
url = {http://dx.doi.org/10.1002/mrm.1910350217},
volume = {35},
year = {1996}
}
@article{Zeiler2014,
abstract = {Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. We also perform an ablation study to discover the performance contribution from different model layers. This enables us to find model architectures that outperform Krizhevsky \etal on the ImageNet classification benchmark. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.},
archivePrefix = {arXiv},
arxivId = {arXiv:1311.2901v3},
author = {Zeiler, Matthew D. and Fergus, Rob},
doi = {10.1007/978-3-319-10590-1_53},
eprint = {arXiv:1311.2901v3},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Zeiler, Fergus - 2014 - Visualizing and understanding convolutional networks.pdf:pdf},
isbn = {9783319105895},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
number = {PART 1},
pages = {818--833},
title = {{Visualizing and understanding convolutional networks}},
volume = {8689 LNCS},
year = {2014}
}
@article{Tommy,
author = {Tommy, L},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Tommy - Unknown - Appendix 1 RESEARCH PLAN.pdf:pdf},
number = {7},
pages = {1--5},
title = {{Appendix 1 : RESEARCH PLAN}},
volume = {1}
}
@article{Ibrahim1999,
abstract = {We propose a method for estimating parameters for general parametric regression models with an arbitrary number of missing covariates. We allow any pattern of missing data and assume that the missing data mechanism is ignorable throughout. When the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM algorithm by the method of weights proposed in Ibrahim (1990, Journal of the American Statistical Association 85, 765-769). We extend this method to continuous or mixed categorical and continuous covariates, and for arbitrary parametric regression models, by adapting a Monte Carlo version of the EM algorithm as discussed by Wei and Tanner (1990, Journal of the American Statistical Association 85, 699-704). In addition, we discuss the Gibbs sampler for sampling from the conditional distribution of the missing covariates given the observed data and show that the appropriate complete conditionals are log-concave. The log-concavity property of the conditional distributions will facilitate a straightforward implementation of the Gibbs sampler via the adaptive rejection algorithm of Gilks and Wild (1992, Applied Statistics 41, 337-348). We assume the model for the response given the covariates is an arbitrary parametric regression model, such as a generalized linear model, a parametric survival model, or a nonlinear model. We model the marginal distribution of the covariates as a product of one-dimensional conditional distributions. This allows us a great deal of flexibility in modeling the distribution of the covariates and reduces the number of nuisance parameters that are introduced in the E-step. We present examples involving both simulated and real data.},
archivePrefix = {arXiv},
arxivId = {1609.04747},
author = {Ruder, Sebastian},
doi = {10.1111/j.0006-341X.1999.00591.x},
eprint = {1609.04747},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ibrahim, Chen, Lipsitz - 1999 - Monte carlo EM for missing covariates in parametric regression models.pdf:pdf},
isbn = {1541-0420},
issn = {0006341X},
journal = {Biometrics},
keywords = {EM algorithm,Generalized linear model,Gibbs sampler,Maximum likelihood estimation,Missing data mechanism,Poisson regression,Proportional hazards,Weibull regression},
number = {2},
pages = {591--596},
pmid = {11318219},
title = {{An overview of gradient descent optimization algorithms}},
url = {http://arxiv.org/abs/1609.04747},
volume = {55},
year = {1999}
}
@article{Roy2011,
abstract = {This paper presents a patch-based non-parametric approach to the correction of intensity inhomogeneity from magnetic resonance (MR) images of the human brain. During image acquisition, the inhomogeneity present in the radio-frequency coil, is usually manifested on the reconstructed MR image as a smooth shading effect. This artifact can significantly deteriorate the performance of any kind of image processing algorithm that uses intensities as a feature. Most of the current inhomogeneity correction techniques use explicit smoothness assumptions on the inhomogeneity field, which sometimes limit their performance if the actual inhomogeneity is not smooth, a problem that becomes prevalent in high fields. The proposed patch-based inhomogeneity correction method does not assume any parametric smoothness model, instead, it uses patches from an atlas of an inhomogeneity-free image to do the correction. Preliminary results show that the proposed method is comparable to N3, a current state of the art method, when the inhomogeneity is smooth, and outperforms N3 when the inhomogeneity contains non-smooth elements.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Roy, Snehashis and Carass, Aaron and Bazin, Pierre-Louis and Prince, Jerry L.},
doi = {10.1117/12.877466},
eprint = {NIHMS150003},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Roy et al. - 2011 - Intensity inhomogeneity correction of magnetic resonance images using patches.pdf:pdf},
isbn = {9780819485045},
issn = {0277-786X},
journal = {Medical Imaging 2011: Image Processing},
keywords = {MRI,RF field inhomogeneity,artifact correction,bias field,gain field,intensity inhomogeneity,non-uniformity,patch,segmentation},
pages = {79621F},
pmid = {25077011},
title = {{Intensity inhomogeneity correction of magnetic resonance images using patches}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/25077011%0Ahttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4112202%0Ahttp://www.ncbi.nlm.nih.gov/pubmed/25077011%5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4112202},
volume = {7962},
year = {2011}
}
@article{Martin2001,
abstract = {This paper presents a database containing `ground truth'\nsegmentations produced by humans for images of a wide variety of natural\nscenes. We define an error measure which quantifies the consistency\nbetween segmentations of differing granularities and find that different\nhuman segmentations of the same image are highly consistent. Use of this\ndataset is demonstrated in two applications: (1) evaluating the\nperformance of segmentation algorithms and (2) measuring probability\ndistributions associated with Gestalt grouping factors as well as\nstatistics of image region properties},
author = {Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
doi = {10.1109/ICCV.2001.937655},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Martin et al. - 2001 - A database of human segmented natural images and its application to evaluating segmentation algorithms and measur.pdf:pdf},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
number = {July},
pages = {416--423},
title = {{A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics}},
volume = {2},
year = {2001}
}
@article{Dozat2016,
author = {Dozat, Timothy},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Dozat - 2016 - Incorporating Nesterov Momentum into Adam.pdf:pdf},
journal = {ICLR Workshop},
title = {{Incorporating Nesterov Momentum into Adam}},
year = {2016}
}
@article{Du2018,
archivePrefix = {arXiv},
arxivId = {1811.03804},
author = {Du, Simon S. and Lee, Jason D. and Li, Haochuan and Wang, Liwei and Zhai, Xiyu},
doi = {arXiv:1811.03804v1},
eprint = {1811.03804},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Du et al. - 2018 - Gradient Descent Finds Global Minima of Deep Neural Networks.pdf:pdf},
isbn = {1811.03804v2},
title = {{Gradient Descent Finds Global Minima of Deep Neural Networks}},
url = {http://arxiv.org/abs/1811.03804},
year = {2018}
}
@article{Lucic2019,
abstract = {Deep generative models are becoming a cornerstone of modern machine learning. Recent work on conditional generative adversarial networks has shown that learning complex, high-dimensional distributions over natural images is within reach. While the latest models are able to generate high-fidelity, diverse natural images at high resolution, they rely on a vast quantity of labeled data. In this work we demonstrate how one can benefit from recent work on self- and semi-supervised learning to outperform the state of the art on both unsupervised ImageNet synthesis, as well as in the conditional setting. In particular, the proposed approach is able to match the sample quality (as measured by FID) of the current state-of-the-art conditional model BigGAN on ImageNet using only 10% of the labels and outperform it using 20% of the labels.},
archivePrefix = {arXiv},
arxivId = {arXiv:1903.02271v2},
author = {Lucic, Mario and Tschannen, Michael and Ritter, Marvin and Zhai, Xiaohua and Bachem, Olivier and Gelly, Sylvain},
eprint = {arXiv:1903.02271v2},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lucic et al. - 2019 - High-fidelity image generation with fewer labels.pdf:pdf},
isbn = {9781510886988},
journal = {36th International Conference on Machine Learning, ICML 2019},
pages = {7392--7414},
title = {{High-fidelity image generation with fewer labels}},
volume = {2019-June},
year = {2019}
}
@article{Eigen2014,
abstract = {What use can the brain make of the massive flow of sensory information that occurs without any associated rewards or punishments? This question is reviewed in the light of connectionist models of unsupervised learning and some older ideas, namely the cognitive maps and working models of Tolman and Craik, and the idea that redundancy is important for understanding perception (Attneave 1954), the physiology of sensory pathways (Barlow 1959), and pattern recognition (Watanabe 1960). It is argued that (1) The redundancy of sensory messages provides the knowledge incorporated in the maps or models. (2) Some of this knowledge can be obtained by observations of mean, variance, and covariance of sensory messages, and perhaps also by a method called “minimum entropy coding.” (3) Such knowledge may be incorporated in a model of “what usually happens” with which incoming messages are automatically compared, enabling unexpected discrepancies to be immediately identified. (4) Knowledge of the sort incorporated into su...},
archivePrefix = {arXiv},
arxivId = {1512.00567},
author = {Eigen, David and Puhrsch, Christian and Fergus, Rob},
doi = {10.1017/CBO9781139342674.012},
eprint = {1512.00567},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Eigen, Puhrsch, Fergus - 2014 - Depth Map Prediction from a Single Image using a Multi-Scale Deep Network.pdf:pdf},
isbn = {9781139342674},
issn = {16137736},
journal = {Predictive Modeling Applications in Actuarial Science: Volume I: Predictive Modeling Techniques},
keywords = {Finance and accountancy,Finance and insurance,Statistics for econometrics},
pages = {280--312},
pmid = {20879316},
title = {{Depth Map Prediction from a Single Image using a Multi-Scale Deep Network}},
url = {http://arxiv.org/abs/1406.2283},
year = {2014}
}
@article{Lyu2020,
author = {Lyu, Qing and Member, Student and Shan, Hongming and Steber, Cole and Helis, Corbin and Whitlow, Chris and Chan, Michael and Wang, Ge},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lyu et al. - 2020 - Multi-Contrast Super-Resolution MRI Through a Progressive Network.pdf:pdf},
number = {9},
pages = {2738--2749},
title = {{Multi-Contrast Super-Resolution MRI Through a Progressive Network}},
volume = {39},
year = {2020}
}
@book{JoshuaSchimel2011,
author = {{Joshua Schimel}},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Joshua Schimel - 2011 - Writing Science_ How to Write Papers That Get Cited and Proposals That Get Funded-Oxford University Press,(2011).pdf:pdf},
isbn = {9780199760237},
pages = {236},
title = {{Writing Science_ How to Write Papers That Get Cited and Proposals That Get Funded-Oxford University Press, USA (2011)}},
year = {2011}
}
@article{Agapiou2014,
abstract = {We consider a class of linear ill-posed inverse problems arising from inversion of a compact operator with singular values which decay exponentially to zero. We adopt a Bayesian approach, assuming a Gaussian prior on the unknown function. The observational noise is assumed to be Gaussian; as a consequence the prior is conjugate to the likelihood so that the posterior distribution is also Gaussian. We study Bayesian posterior consistency in the small observational noise limit. We assume that the forward operator and the prior and noise covariance operators commute with one another. We show how, for given smoothness assumptions on the truth, the scale parameter of the prior, which is a constant multiplier of the prior covariance operator, can be adjusted to optimize the rate of posterior contraction to the truth, and we explicitly compute the logarithmic rate. {\textcopyright} 2014 by Walter de Gruyter Berlin/Boston 2014.},
author = {Agapiou, Sergios and Stuart, Andrew M. and Zhang, Yuan Xiang},
doi = {10.1515/jip-2012-0071},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Agapiou, Stuart, Zhang - 2014 - Bayesian posterior contraction rates for linear severely ill-posed inverse problems.pdf:pdf},
issn = {09280219},
journal = {Journal of Inverse and Ill-Posed Problems},
keywords = {Gaussian prior,posterior consistency,rate of contraction,severely ill-posed problems},
number = {3},
pages = {297--321},
title = {{Bayesian posterior contraction rates for linear severely ill-posed inverse problems}},
volume = {22},
year = {2014}
}
@article{Glorot2017,
abstract = {We investigate the polarization-dependent effects when a circularly polarized Airy beam propagates in an inhomogeneous medium. It shows that there are some interesting polarization-dependent phenomena induced by the spin-orbit coupling: the circularly polarized Airy beam will follow a polarization-dependent trajectory in an inhomogeneous medium, and there are the polarization-dependent self-acceleration of the Airy beam in the linear-index inhomogeneous medium and the polarization-dependent rotation of the Airy beam in the quadratic-index inhomogeneous medium. These polarization-dependent phenomena are different from the manifestation of the “traditional” spin Hall effect of a light beam, which just displays the polarization-dependent transverse deflection of the beam.},
author = {Glorot, Xavier and Bordes, Antione and Bengio, Yoshua},
doi = {10.1002/ecs2.1832},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Glorot, Bordes, Bengio - 2011 - Deep Sparse Rectifier Neural Networks.pdf:pdf},
issn = {15208532},
journal = {Journal of the Optical Society of America A: Optics and Image Science, and Vision},
number = {7},
pages = {1114--1118},
title = {{Deep Sparse Rectifier Neural Networks}},
volume = {34},
year = {2011}
}
@article{Kwan1999,
abstract = {With the increased interest in computer-aided image analysis methods, there is a greater need for objective methods of algorithm evaluation. Validation of in vivo MRI studies is complicated by a lack of reference data and the difficulty of constructing anatomically realistic physical phantoms. We present here an extensible MRI simulator that efficiently generates realistic three-dimensional (3-D) brain images using a hybrid Bloch equation and tissue template simulation that accounts for image contast, partial volume, and noise. This allows image analysis methods to be evaluated with controlled degradations of image data.},
author = {Kwan, Remi K.S. and Evans, Alan C. and Pike, Bruce},
doi = {10.1109/42.816072},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kwan, Evans, Pike - 1999 - MRI simulation-based evaluation of image-processing and classification methods.pdf:pdf},
issn = {02780062},
journal = {IEEE Transactions on Medical Imaging},
number = {11},
pages = {1085--1097},
title = {{MRI simulation-based evaluation of image-processing and classification methods}},
volume = {18},
year = {1999}
}
@article{Chen2019,
abstract = {Conditional GANs are at the forefront of natural image synthesis. The main drawback of such models is the necessity for labeled data. In this work we exploit two popular unsupervised learning techniques, adversarial training and self-supervision, and take a step towards bridging the gap between conditional and unconditional GANs. In particular, we allow the networks to collaborate on the task of representation learning, while being adversarial with respect to the classic GAN game. The role of self-supervision is to encourage the discriminator to learn meaningful feature representations which are not forgotten during training. We test empirically both the quality of the learned image representations, and the quality of the synthesized images. Under the same conditions, the self-supervised GAN attains a similar performance to state-of-the-art conditional counterparts. Finally, we show that this approach to fully unsupervised learning can be scaled to attain an FID of 23.4 on unconditional ImageNet generation.},
author = {Chen, Ting and Zhai, Xiaohua and Ritter, Marvin and Lucic, Mario and Houlsby, Neil},
doi = {10.1109/CVPR.2019.01243},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Chen et al. - 2019 - Self-supervised gans via auxiliary rotation loss.pdf:pdf},
isbn = {9781728132938},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
keywords = {Deep Learning,Image and Video Synthesis,Representation Learning},
pages = {12146--12155},
title = {{Self-supervised gans via auxiliary rotation loss}},
volume = {2019-June},
year = {2019}
}
@article{Bernal2019,
abstract = {In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation. These methods have also been utilised in medical image analysis domain for lesion segmentation, anatomical segmentation and classification. We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works. The aim of this study is three-fold. Our primary goal is to report how different CNN architectures have evolved, discuss state-of-the-art strategies, condense their results obtained using public datasets and examine their pros and cons. Second, this paper is intended to be a detailed reference of the research activity in deep CNN for brain MRI analysis. Finally, we present a perspective on the future of CNNs in which we hint some of the research directions in subsequent years.},
archivePrefix = {arXiv},
arxivId = {1712.03747},
author = {Bernal, Jose and Kushibar, Kaisar and Asfaw, Daniel S. and Valverde, Sergi and Oliver, Arnau and Mart{\'{i}}, Robert and Llad{\'{o}}, Xavier},
doi = {10.1016/j.artmed.2018.08.008},
eprint = {1712.03747},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Bernal et al. - 2019 - Deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review.pdf:pdf},
issn = {18732860},
journal = {Artificial Intelligence in Medicine},
keywords = {Brain MRI,Deep convolutional neural network,Review,Segmentation},
number = {August 2018},
pages = {64--81},
publisher = {Elsevier},
title = {{Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review}},
url = {https://doi.org/10.1016/j.artmed.2018.08.008},
volume = {95},
year = {2019}
}
@article{Springenberg2015,
abstract = {In this paper we present a method for learning a discriminative classifier from unlabeled or partially labeled data. Our approach is based on an objective function that trades-off mutual information between observed examples and their predicted categorical class distribution, against robustness of the classifier to an adversarial generative model. The resulting algorithm can either be interpreted as a natural generalization of the generative adversarial networks (GAN) framework or as an extension of the regularized information maximization (RIM) framework to robust classification against an optimal adversary. We empirically evaluate our method - which we dub categorical generative adversarial networks (or CatGAN) - on synthetic data as well as on challenging image classification tasks, demonstrating the robustness of the learned classifiers. We further qualitatively assess the fidelity of samples generated by the adversarial generator that is learned alongside the discriminative classifier, and identify links between the CatGAN objective and discriminative clustering algorithms (such as RIM).},
archivePrefix = {arXiv},
arxivId = {1511.06390},
author = {Springenberg, Jost Tobias},
eprint = {1511.06390},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Springenberg - 2015 - Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks.pdf:pdf},
number = {2009},
pages = {1--20},
title = {{Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks}},
url = {http://arxiv.org/abs/1511.06390},
year = {2015}
}
@article{Karnewar2020,
abstract = {While Generative Adversarial Networks (GANs) have seen huge successes in image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due to instability during training and sensitivity to hyperparameters. One commonly accepted reason for this instability is that gradients passing from the discriminator to the generator become uninformative when there isn't enough overlap in the supports of the real and fake distributions. In this work, we propose the Multi-Scale Gradient Generative Adversarial Network (MSG-GAN), a simple but effective technique for addressing this by allowing the flow of gradients from the discriminator to the generator at multiple scales. This technique provides a stable approach for high resolution image synthesis, and serves as an alternative to the commonly used progressive growing technique. We show that MSG-GAN converges stably on a variety of image datasets of different sizes, resolutions and domains, as well as different types of loss functions and architectures, all with the same set of fixed hyperparameters. When compared to state-of-the-art GANs, our approach matches or exceeds the performance in most of the cases we tried.},
archivePrefix = {arXiv},
arxivId = {1903.06048},
author = {Karnewar, Animesh and Wang, Oliver},
doi = {10.1109/cvpr42600.2020.00782},
eprint = {1903.06048},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Karnewar, Wang - 2020 - MSG-GAN Multi-Scale Gradients for Generative Adversarial Networks.pdf:pdf},
pages = {7796--7805},
title = {{MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks}},
year = {2020}
}
@article{Tustison2009,
abstract = {Several algorithms exist for correcting the nonuniform intensity in magnetic resonance images caused by ﬁeld inhomogeneities.1 These algorithms constitute impor tant preprocessing steps for subsequent image analysis tasks. One such algorithm, known as parametric bias ﬁeld correction (PABIC) [2], has already been implemented in ITK. Another popular algorithm is the non-uniform intensity normalization (N3) approach [1]. A par ticularly advantageous aspect of this algorithm is that it does not require a prior tissue model for its application. In addition, the source code for N3},
author = {Tustison, Nicholas James and Gee, James C},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Tustison, Gee - 2009 - N4ITK Nick's N3 ITK Implementation For MRI Bias Field Correction.pdf:pdf},
journal = {InsightJournal},
pages = {1--8},
title = {{N4ITK: Nick's N3 ITK Implementation For MRI Bias Field Correction}},
url = {http://www.insight-journal.org/browse/publication/640%5Cnpapers2://publication/uuid/BB26233C-D637-405C-A49E-4E487427AE18%5Cnpapers2://publication/uuid/D6B8F005-7D75-45B7-8014-172867966A98},
year = {2009}
}
@article{Gandelsman2018,
abstract = {Many seemingly unrelated computer vision tasks can be viewed as a special case of image decomposition into separate layers. For example, image segmentation (separation into foreground and background layers); transparent layer separation (into reflection and transmission layers); Image dehazing (separation into a clear image and a haze map), and more. In this paper we propose a unified framework for unsupervised layer decomposition of a single image, based on coupled "Deep-image-Prior" (DIP) networks. It was shown [Ulyanov et al] that the structure of a single DIP generator network is sufficient to capture the low-level statistics of a single image. We show that coupling multiple such DIPs provides a powerful tool for decomposing images into their basic components, for a wide variety of applications. This capability stems from the fact that the internal statistics of a mixture of layers is more complex than the statistics of each of its individual components. We show the power of this approach for Image-Dehazing, Fg/Bg Segmentation, Watermark-Removal, Transparency Separation in images and video, and more. These capabilities are achieved in a totally unsupervised way, with no training examples other than the input image/video itself.},
archivePrefix = {arXiv},
arxivId = {1812.00467},
author = {Gandelsman, Y. and Shocher, A. and Irani, M.},
eprint = {1812.00467},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gandelsman, Shocher, Irani - 2018 - Double-DIP Unsupervised Image Decomposition via Coupled Deep-Image-Priors.pdf:pdf},
number = {788535},
title = {{"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors}},
url = {http://arxiv.org/abs/1812.00467},
year = {2018}
}
@article{Widmark2019,
abstract = {BACKGROUND Hypofractionated radiotherapy for prostate cancer has gained increased attention due to its proposed high radiation-fraction sensitivity. Recent reports from studies comparing moderately hypofractionated and conventionally fractionated radiotherapy support the clinical use of moderate hypofractionation. To date, there are no published randomised studies on ultra-hypofractionated radiotherapy. Here, we report the outcomes of the Scandinavian HYPO-RT-PC phase 3 trial with the aim to show non-inferiority of ultra-hypofractionation compared with conventional fractionation. METHODS In this open-label, randomised, phase 3 non-inferiority trial done in 12 centres in Sweden and Denmark, we recruited men up to 75 years of age with intermediate-to-high-risk prostate cancer and a WHO performance status between 0 and 2. Patients were randomly assigned to ultra-hypofractionation (42{\textperiodcentered}7 Gy in seven fractions, 3 days per week for 2{\textperiodcentered}5 weeks) or conventional fractionated radiotherapy (78{\textperiodcentered}0 Gy in 39 fractions, 5 days per week for 8 weeks). No androgen deprivation therapy was allowed. The primary endpoint was time to biochemical or clinical failure, analysed in the per-protocol population. The prespecified non-inferiority margin was 4% at 5 years, corresponding to a critical hazard ratio (HR) limit of 1{\textperiodcentered}338. Physician-recorded toxicity was measured according to the Radiation Therapy Oncology Group (RTOG) morbidity scale and patient-reported outcome measurements with the Prostate Cancer Symptom Scale (PCSS) questionnaire. This trial is registered with the ISRCTN registry, number ISRCTN45905321. FINDINGS Between July 1, 2005, and Nov 4, 2015, 1200 patients were randomly assigned to conventional fractionation (n=602) or ultra-hypofractionation (n=598), of whom 1180 (591 conventional fractionation and 589 ultra-hypofractionation) constituted the per-protocol population. 1054 (89%) participants were intermediate risk and 126 (11%) were high risk. Median follow-up time was 5{\textperiodcentered}0 years (IQR 3{\textperiodcentered}1-7{\textperiodcentered}0). The estimated failure-free survival at 5 years was 84% (95% CI 80-87) in both treatment groups, with an adjusted HR of 1{\textperiodcentered}002 (95% CI 0{\textperiodcentered}758-1{\textperiodcentered}325; log-rank p=0{\textperiodcentered}99). There was weak evidence of an increased frequency of acute physician-reported RTOG grade 2 or worse urinary toxicity in the ultra-hypofractionation group at end of radiotherapy (158 [28%] of 569 patients vs 132 [23%] of 578 patients; p=0{\textperiodcentered}057). There were no significant differences in grade 2 or worse urinary or bowel late toxicity between the two treatment groups at any point after radiotherapy, except for an increase in urinary toxicity in the ultra-hypofractionation group compared to the conventional fractionation group at 1-year follow-up (32 [6%] of 528 patients vs 13 [2%] of 529 patients; (p=0{\textperiodcentered}0037). We observed no differences between groups in frequencies at 5 years of RTOG grade 2 or worse urinary toxicity (11 [5%] of 243 patients for the ultra-hypofractionation group vs 12 [5%] of 249 for the conventional fractionation group; p=1{\textperiodcentered}00) and bowel toxicity (three [1%] of 244 patients vs nine [4%] of 249 patients; p=0{\textperiodcentered}14). Patient-reported outcomes revealed significantly higher levels of acute urinary and bowel symptoms in the ultra-hypofractionation group compared with the conventional fractionation group but no significant increases in late symptoms were found, except for increased urinary symptoms at 1-year follow-up, consistent with the physician-evaluated toxicity. INTERPRETATION Ultra-hypofractionated radiotherapy is non-inferior to conventionally fractionated radiotherapy for intermediate-to-high risk prostate cancer regarding failure-free survival. Early side-effects are more pronounced with ultra-hypofractionation compared with conventional fractionation whereas late toxicity is similar in both treatment groups. The results support the use of ultra-hypofractionation for radiotherapy of prostate cancer. FUNDING The Nordic Cancer Union, the Swedish Cancer Society, and the Swedish Research Council.},
author = {Widmark, Anders and Gunnlaugsson, Adalsteinn and Beckman, Lars and Thellenberg-Karlsson, Camilla and Hoyer, Morten and Lagerlund, Magnus and Kindblom, Jon and Ginman, Claes and Johansson, Bengt and Bj{\"{o}}rnlinger, Kirsten and Seke, Mihajl and Agrup, M{\aa}ns and Fransson, Per and Tavelin, Bj{\"{o}}rn and Norman, David and Zackrisson, Bj{\"{o}}rn and Anderson, Harald and Kjell{\'{e}}n, Elisabeth and Franz{\'{e}}n, Lars and Nilsson, Per},
doi = {10.1016/s0140-6736(19)31131-6},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Widmark et al. - 2019 - Ultra-hypofractionated versus conventionally fractionated radiotherapy for prostate cancer 5-year outcomes of th.pdf:pdf},
issn = {01406736},
journal = {The Lancet},
number = {10196},
pages = {385--395},
title = {{Ultra-hypofractionated versus conventionally fractionated radiotherapy for prostate cancer: 5-year outcomes of the HYPO-RT-PC randomised, non-inferiority, phase 3 trial}},
volume = {394},
year = {2019}
}
@article{Weiner2009,
abstract = {We describe an improved method of measuring brain atrophy rates from serial MRI for multi-site imaging studies of Alzheimer's disease (AD). The method (referred to as KN-BSI) improves an existing brain atrophy measurement technique-the boundary shift integral (classic-BSI), by performing tissue-specific intensity normalization and parameter selection. We applied KN-BSI to measure brain atrophy rates of 200 normal and 141 AD subjects using baseline and 1-year MRI scans downloaded from the Alzheimer's Disease Neuroimaging Initiative database. Baseline and repeat images were reviewed as pairs by expert raters and given quality scores. Including all image pairs, regardless of quality score, mean KN-BSI atrophy rates were 0.09% higher (95% CI 0.03% to 0.16%, p=0.007) than classic-BSI rates in controls and 0.07% higher (-0.01% to 0.16%, p=0.07) higher in ADs. The SD of the KN-BSI rates was 22% lower (15% to 29%, pb0.001) in controls and 13% lower (6% to 20%, p=0.001) in ADs, compared to classic-BSI. Using these results, the estimated sample size (needed per treatment arm) for a hypothetical trial of a treatment for AD (80% power, 5% significance to detect a 25% reduction in atrophy rate) would be reduced from 120 to 81 (a 32% reduction, 95% CI=18% to 45%, pb0.001) when using KN-BSI instead of classic-BSI. We concluded that KN-BSI offers more robust brain atrophy measurement than classic-BSI and substantially reduces sample sizes needed in clinical trials. {\textcopyright} 2010 Elsevier Inc. All rights reserved.},
author = {Leung, Kelvin K. and Clarkson, Matthew J. and Bartlett, Jonathan W. and Clegg, Shona and Jack, Clifford R. and Weiner, Michael W. and Fox, Nick C. and Ourselin, S{\'{e}}bastien},
doi = {10.1016/j.neuroimage.2009.12.059},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Weiner et al. - 2009 - Robust atrophy rate measurement in Alzheimer's disease using multi-site serial MRI Tissue-specific intensity norm.pdf:pdf},
issn = {10538119},
journal = {NeuroImage},
keywords = {Alzheimer's disease,Atrophy,BSI,Boundary shift integral,Intensity normalization,KN-BSI,MRI},
number = {2},
pages = {516--523},
publisher = {Elsevier Inc.},
title = {{Robust atrophy rate measurement in Alzheimer's disease using multi-site serial MRI: Tissue-specific intensity normalization and parameter selection}},
url = {http://dx.doi.org/10.1016/j.neuroimage.2009.12.059},
volume = {50},
year = {2010}
}
@article{Molchanov2017,
abstract = {We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case when dropout rates are unbounded, propose a way to reduce the variance of the gradient estimator and report first experimental results with individual dropout rates per weight. Interestingly, it leads to extremely sparse solutions both in fully-connected and convolutional layers. This effect is similar to automatic relevance determination effect in empirical Bayes but has a number of advantages. We reduce the number of parameters up to 280 times on LeNet architectures and up to 68 times on VGG-like networks with a negligible decrease of accuracy.},
archivePrefix = {arXiv},
arxivId = {1701.05369},
author = {Glorot, Xavier and Bengio, Yoshua},
eprint = {1701.05369},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Molchanov, Ashukha, Vetrov - 2017 - Variational Dropout Sparsifies Deep Neural Networks.pdf:pdf},
pages = {249--256},
title = {{Understanding the difficulty of training deep feedforward neural networks Xavier}},
url = {http://arxiv.org/abs/1701.05369},
volume = {9},
year = {2017}
}
@article{Myronenko2019,
abstract = {Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease. Manual delineation practices require anatomical knowledge, are expensive, time consuming and can be inaccurate due to human error. Here, we describe a semantic segmentation network for tumor subregion segmentation from 3D MRIs based on encoder-decoder architecture. Due to a limited training dataset size, a variational auto-encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers. The current approach won 1st place in the BraTS 2018 challenge.},
archivePrefix = {arXiv},
arxivId = {arXiv:1810.11654v3},
author = {Myronenko, A.},
doi = {10.1007/978-3-030-11726-9_28},
eprint = {arXiv:1810.11654v3},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Myronenko - 2019 - 3D MRI brain tumor segmentation using autoencoder regularization.pdf:pdf},
isbn = {9783030117252},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {311--320},
title = {{3D MRI brain tumor segmentation using autoencoder regularization}},
volume = {11384 LNCS},
year = {2019}
}
@article{Lempitsky2018,
abstract = {Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs. Apart from its diverse applications, our approach highlights the inductive bias captured by standard generator network architectures. It also bridges the gap between two very popular families of image restoration methods: learning-based methods using deep convolutional networks and learning-free methods based on handcrafted image priors such as self-similarity. Code and supplementary material are available at https://dmitryulyanov.github.io/deep_image_prior .},
archivePrefix = {arXiv},
arxivId = {arXiv:1711.10925v3},
author = {Lempitsky, Victor and Vedaldi, Andrea and Ulyanov, Dmitry},
doi = {10.1109/CVPR.2018.00984},
eprint = {arXiv:1711.10925v3},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lempitsky, Vedaldi, Ulyanov - 2018 - Deep Image Prior.pdf:pdf},
isbn = {9781538664209},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {9446--9454},
title = {{Deep Image Prior}},
year = {2018}
}
@article{Shaikhina2017,
abstract = {Motivation Single-centre studies in medical domain are often characterised by limited samples due to the complexity and high costs of patient data collection. Machine learning methods for regression modelling of small datasets (less than 10 observations per predictor variable) remain scarce. Our work bridges this gap by developing a novel framework for application of artificial neural networks (NNs) for regression tasks involving small medical datasets. Methods In order to address the sporadic fluctuations and validation issues that appear in regression NNs trained on small datasets, the method of multiple runs and surrogate data analysis were proposed in this work. The approach was compared to the state-of-the-art ensemble NNs; the effect of dataset size on NN performance was also investigated. Results The proposed framework was applied for the prediction of compressive strength (CS) of femoral trabecular bone in patients suffering from severe osteoarthritis. The NN model was able to estimate the CS of osteoarthritic trabecular bone from its structural and biological properties with a standard error of 0.85 MPa. When evaluated on independent test samples, the NN achieved accuracy of 98.3%, outperforming an ensemble NN model by 11%. We reproduce this result on CS data of another porous solid (concrete) and demonstrate that the proposed framework allows for an NN modelled with as few as 56 samples to generalise on 300 independent test samples with 86.5% accuracy, which is comparable to the performance of an NN developed with 18 times larger dataset (1030 samples). Conclusion The significance of this work is two-fold: the practical application allows for non-destructive prediction of bone fracture risk, while the novel methodology extends beyond the task considered in this study and provides a general framework for application of regression NNs to medical problems characterised by limited dataset sizes.},
author = {Shaikhina, Torgyn and Khovanova, Natalia A.},
doi = {10.1016/j.artmed.2016.12.003},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Shaikhina, Khovanova - 2017 - Handling limited datasets with neural networks in medical applications A small-data approach.pdf:pdf},
isbn = {1873-2860 (Electronic) 0933-3657 (Linking)},
issn = {18732860},
journal = {Artificial Intelligence in Medicine},
keywords = {Compressive strength,Osteoarthritis,Predictive modelling,Regression neural networks,Small data,Trabecular bone},
pages = {51--63},
pmid = {28363456},
publisher = {Elsevier B.V.},
title = {{Handling limited datasets with neural networks in medical applications: A small-data approach}},
url = {http://dx.doi.org/10.1016/j.artmed.2016.12.003},
volume = {75},
year = {2017}
}
@article{Rusinek2020,
author = {Rusinek, Henry},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Rusinek - 2020 - Retrospective MRI non-uniformity correction quantitative assessment of two methods.pdf:pdf},
number = {August},
title = {{Retrospective MRI non-uniformity correction : quantitative assessment of two methods}},
year = {2020}
}
@article{Ren2019,
abstract = {In this paper, we propose a novel oversampling strategy dubbed Entropy-based Wasserstein Generative Adversarial Network (EWGAN) to generate data samples for minority classes in imbalanced learning. First, we construct an entropyweighted label vector for each class to characterize the data imbalance in different classes. Then we concatenate this entropyweighted label vector with the original feature vector of each data sample, and feed it into the WGAN model to train the generator. After the generator is trained, we concatenate the entropy-weighted label vector with random noise feature vectors, and feed them into the generator to generate data samples for minority classes. Experimental results on two benchmark datasets show that the samples generated by the proposed oversampling strategy can help to improve the classification performance when the data are highly imbalanced. Furthermore, the proposed strategy outperforms other state-of-the-art oversampling algorithms in terms of the classification accuracy.},
author = {Ren, Jinfu and Liu, Yang and Liu, Jiming},
doi = {10.1609/aaai.v33i01.330110011},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ren, Liu, Liu - 2019 - EWGAN Entropy-Based Wasserstein GAN for Imbalanced Learning.pdf:pdf},
issn = {2159-5399},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
keywords = {Document Embedding, Crossword Prediction, Encoder-},
pages = {10011--10012},
title = {{EWGAN: Entropy-Based Wasserstein GAN for Imbalanced Learning}},
volume = {33},
year = {2019}
}
@article{Conrad1998,
abstract = {In this article we use a single unifying framework to an- alyze the sources of profits to a wide spectrum of return- based trading strategies implemented in the literature. We show that less than 50% of the 120 strategies imple- mented in the article yield statistically significant profits and, unconditionally, momentum and contrarian strate- gies are equally likely to be successful. However, when we condition on the return horizon (short, medium, or long) of the strategy, or the time period during which it is im- plemented, two patterns emerge. A momentum strategy is usually profitable at the medium (3- to 12-month) hori- zon, while a contrarian strategy nets statistically signifi- cant profits at long horizons, but only during the 1926- 1947 subperiod. More importantly, our results show that the cross-sectional variation in the mean returns of in- dividual securities included in these strategies plays an important role in their profitability. The cross-sectional variation can potentially account for the profitability of momentum strategies and it is also responsible for atten-},
author = {Conrad, Jennifer and Kaul, Gautam},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Conrad, Kaul - 1998 - The Society for Financial Studies An Anatomy of Trading Strategies Author ( s ) Jennifer Conrad and Gautam Kaul So.pdf:pdf},
journal = {The Review of Financial Studies, Oxford University Press},
number = {3},
pages = {489--519},
title = {{The Society for Financial Studies An Anatomy of Trading Strategies Author ( s ): Jennifer Conrad and Gautam Kaul Source : The Review of Financial Studies , Vol . 11 , No . 3 ( Autumn , 1998 ), pp . 489-519 Published by : . Sponsor}},
volume = {11},
year = {1998}
}
@book{Jha2012,
abstract = {We proposed WiMAX network for Electronic Governance (EG) for rural areas. WiMAX will great replacement in rural areas with cost effective and end to end solution (e-healthcare is biggest example). The Empowerment of Rural communities is crucial for the development of the rural region. Bringing the rural people in to the mainstream of the advanced /modern technologies for the assessing the impacts of climate change and litigation are a major concern now. Rural Development implies both, the economic development of the people and greater social transformation with the help of communication and information technology, electronic governance is possible in rural area. In order to provide the rural people with better prospects and opportunities for economic development, agriculture development and management, Agriculture marketing management increased participation of farmers in electronic governance through information and communication technologies are envisaged. This paper will examine the current status of electronic governance in India in different rural regions and different technologies involved in e-governance process for the assessment of Agriculture and rural developments. Next generation Network (NGN) should be a part of rural area network solution in next decayed. This paper will focus on analysis on problem statement, restraining forces and driving forces available in Jalgoan district and how to utilize driving forces to design and develop the model for the e-governance to bring socio-economic developed and good governance in rural region of Jalgoan district in Maharashtra state, India. {\textcopyright} 2012 Springer-Verlag.},
author = {Jha, Rakesh Kumar and Dalal, Upena D.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-642-29280-4},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Jha, Dalal - 2012 - Advanced Computing, Networking and Security.pdf:pdf},
isbn = {978-3-642-29279-8},
issn = {03029743},
keywords = {Application of Information Technology,E-Governance,OPNET MODELER 14.5,WiMAX},
pages = {279--288},
title = {{Advanced Computing, Networking and Security}},
url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84859587825&partnerID=tZOtx3y1},
volume = {7135},
year = {2012}
}
@article{Jelenkor2019a,
author = {Jelenkor},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Jelenkor - 2019 - Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat 19078.pdf:pdf},
title = {{Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat 1907/8}},
year = {2019}
}
@article{Weisenfeld2004,
abstract = {We describe a novel algorithm for altering global statistics of magnetic resonance images (MRI) to fit a model distribution while preserving local feature contrast. Our algorithm estimates a multiplicative correction field that alters the intensity statistics of an image or set of images to best match those of a model. This is achieved by minimizing the Kullback-Leibler divergence between the observed and desired intensity distributions. This procedure is effective for the discovery and removal of undesirable intra-individual and inter-individual signal intensity changes caused by developmental processes, disease processes or MR scanner intensity artifacts. Ultimately our goal is to improve the quality of segmentations obtained by classification of tissues on the basis of signal intensities by removing undesirable signal differences both within a subject, where tissue of the same composition may image differently in different parts of the acquisition volume, and between subjects in cases where both inter-subject and inter-acquisition variability are confounds. Validation experiments with synthetic data indicate the algorithm can successfully remove typical signal intensity inhomogeneities, and illustrative results demonstrate successful intensity normalization applied to a segmentation problem. {\textcopyright}2004 IEEE.},
author = {Weisenfeld, N. I. and Warfield, S. K.},
doi = {10.1109/ISBI.2004.1398484},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Weisenfeld, Warfield - 2004 - Normalization of joint image-intensity statistics in MRI using the Kullback-Leibler divergence.pdf:pdf},
isbn = {0780383885},
journal = {2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano},
pages = {101--104},
title = {{Normalization of joint image-intensity statistics in MRI using the Kullback-Leibler divergence}},
volume = {1},
year = {2004}
}
@article{Li2016,
abstract = {We present a novel physics-inspired neural network (Pi-NN) approach for compact modeling. Development of high-quality compact models for devices is a key to connect device science with applications. One recent approach is to treat compact modeling as a regression problem in machine learning. The most common learning algorithm to develop compact models is the multilayer perceptron (MLP) neural network. However, device compact models derived using the MLP neural networks often exhibit unphysical behavior, which is eliminated in the Pi-NN approach proposed in this paper, since the Pi-NN incorporates fundamental device physics. As a result, smooth, accurate, and computationally efficient device models can be learned from discrete data points by using Pi-NN. This paper sheds new light on the future of the neural network compact modeling. INDEX TERMS Supervised learning, artificial neural networks, semiconductor device modeling, TFETs.},
author = {Li, Mingda and Irsoy, Ozan and Cardie, Claire and Xing, Huili Grace},
doi = {10.1109/jxcdc.2016.2636161},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Li et al. - 2016 - Physics-Inspired Neural Networks for Efficient Device Compact Modeling.pdf:pdf},
issn = {2329-9231},
journal = {IEEE Journal on Exploratory Solid-State Computational Devices and Circuits},
number = {November 2018},
pages = {44--49},
title = {{Physics-Inspired Neural Networks for Efficient Device Compact Modeling}},
volume = {2},
year = {2016}
}
@article{Giacomello2019,
abstract = {In this work, we present an approach to brain cancer segmentation in Magnetic Resonance Images (MRI) using Adversarial Networks, that have been successfully applied to several complex image processing problems in recent years. Most of the segmentation approaches presented in the literature exploit the data from all the contrast modalities typically acquired in the clinical practice: T1-weighted, T1-weighted contrast-enhanced, T2-weighted, and T2-FLAIR. Unfortunately, often not all these modalities are available for each patient. Accordingly, in this paper, we extended a previous segmentation approach based on Adversarial Networks to deal with this issue. In particular, we trained a segmentation model for each modality at once and evaluated the performances of these models. Thus, we investigated the possibility of transferring the best among these single-modality models to the other modalities. Our results suggest that such a transfer learning approach allows achieving better performances for almost all the target modalities.},
archivePrefix = {arXiv},
arxivId = {1910.02717},
author = {Giacomello, Edoardo and Loiacono, Daniele and Mainardi, Luca},
eprint = {1910.02717},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Giacomello, Loiacono, Mainardi - 2019 - Transfer Brain MRI Tumor Segmentation Models Across Modalities with Adversarial Networks.pdf:pdf},
isbn = {9781728118840},
pages = {0--4},
title = {{Transfer Brain MRI Tumor Segmentation Models Across Modalities with Adversarial Networks}},
url = {http://arxiv.org/abs/1910.02717},
year = {2019}
}
@article{Locatello2019,
abstract = {The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train more than 12 000 models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on seven different data sets. We observe that while the different methods successfully enforce properties "encouraged" by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.},
archivePrefix = {arXiv},
arxivId = {1811.12359},
author = {Locatello, Francesco and Bauer, Stefan and Lucie, Mario and R{\"{a}}tsch, Gunnar and Gelly, Sylvain and Sch{\"{o}}lkopf, Bernhard and Bachem, Olivier},
eprint = {1811.12359},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Locatello et al. - 2019 - Challenging common assumptions in the unsupervised learning of disentangled representations.pdf:pdf},
isbn = {9781510886988},
journal = {36th International Conference on Machine Learning, ICML 2019},
pages = {7247--7283},
title = {{Challenging common assumptions in the unsupervised learning of disentangled representations}},
volume = {2019-June},
year = {2019}
}
@article{Authors,
author = {Authors, Anonymous},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Authors - Unknown - Simplicity bias in the parameter-function map of deep neural networks.pdf:pdf},
number = {Icml},
title = {{Simplicity bias in the parameter-function map of deep neural networks}}
}
@article{Gulrajani2017,
abstract = {Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only poor samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models with continuous generators. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms.},
archivePrefix = {arXiv},
arxivId = {1704.00028},
author = {Gulrajani, Ishaan and Ahmed, Faruk and Arjovsky, Martin and Dumoulin, Vincent and Courville, Aaron},
eprint = {1704.00028},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gulrajani et al. - 2017 - Improved training of wasserstein GANs.pdf:pdf},
issn = {10495258},
journal = {Advances in Neural Information Processing Systems},
pages = {5768--5778},
title = {{Improved training of wasserstein GANs}},
volume = {2017-Decem},
year = {2017}
}
@article{Evans,
author = {Evans, Alan C and Pike, G Bruce and Brain, Mcconnell and Centre, Imaging},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Evans et al. - Unknown - A n Extensible M R I Simulator for Post-processing Evaluation.pdf:pdf},
pages = {2--7},
title = {{An Extensible MRI Simulator for Post-processing Evaluation}}
}
@article{Hu2017,
archivePrefix = {arXiv},
arxivId = {1709.01507v3},
author = {Hu, J. and Shen, L. and Albanie, S. and Sun, G. and Wu, E.},
doi = {10.1016/j.jen.2016.02.023},
eprint = {1709.01507v3},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hu et al. - 2017 - Squeeze-and-Excitation Networks.pdf:pdf},
isbn = {9781538610329},
issn = {15272966},
journal = {Journal of Emergency Nursing},
number = {3},
pages = {266--267},
pmid = {21178157},
title = {{Squeeze-and-Excitation Networks}},
volume = {43},
year = {2017}
}
@article{Bott2014,
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Ioffe, Sergey and Szegedy, Christian},
doi = {10.1007/s13398-014-0173-7.2},
eprint = {arXiv:1011.1669v3},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ioffe, Szegedy - 2015 - Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift.pdf:pdf},
isbn = {9780874216561},
issn = {0717-6163},
journal = {Igarss 2014},
keywords = {Bott},
number = {1},
pages = {1--5},
pmid = {15003161},
title = {{Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift}},
volume = {25},
year = {2014}
}
@article{Heller2019,
abstract = {The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment. Quantitative study of the relationship between kidney tumor morphology and clinical outcomes is difficult due to data scarcity and the laborious nature of manually quantifying imaging predictors. Automatic semantic segmentation of kidneys and kidney tumors is a promising tool towards automatically quantifying a wide array of morphometric features, but no sizeable annotated dataset is currently available to train models for this task. We present the KiTS19 challenge dataset: A collection of multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes for 300 patients who underwent nephrectomy for kidney tumors at our center between 2010 and 2018. 210 (70%) of these patients were selected at random as the training set for the 2019 MICCAI KiTS Kidney Tumor Segmentation Challenge and have been released publicly. With the presence of clinical context and surgical outcomes, this data can serve not only for benchmarking semantic segmentation models, but also for developing and studying biomarkers which make use of the imaging and semantic segmentation masks.},
author = {Heller, Nicholas and Sathianathen, Niranjan and Kalapara, Arveen and Walczak, Edward and Moore, Keenan and Kaluzniak, Heather and Rosenberg, Joel and Blake, Paul and Rengel, Zachary and Oestreich, Makinna and Dean, Joshua and Tradewell, Michael and Shah, Aneri and Tejpaul, Resha and Edgerton, Zachary and Peterson, Matthew and Raza, Shaneabbas and Regmi, Subodh and Papanikolopoulos, Nikolaos and Weight, Christopher},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Heller et al. - 2019 - The KiTS19 Challenge Data 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical O.pdf:pdf},
keywords = {kidney tumors,nephrometry,semantic segmentation},
pages = {1--13},
title = {{The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes}},
year = {2019}
}
@article{Wu2018,
abstract = {Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems --- BN's error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN's usage for training larger models and transferring features to computer vision tasks including detection, segmentation, and video, which require small batches constrained by memory consumption. In this paper, we present Group Normalization (GN) as a simple alternative to BN. GN divides the channels into groups and computes within each group the mean and variance for normalization. GN's computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes. On ResNet-50 trained in ImageNet, GN has 10.6% lower error than its BN counterpart when using a batch size of 2; when using typical batch sizes, GN is comparably good with BN and outperforms other normalization variants. Moreover, GN can be naturally transferred from pre-training to fine-tuning. GN can outperform its BN-based counterparts for object detection and segmentation in COCO, and for video classification in Kinetics, showing that GN can effectively replace the powerful BN in a variety of tasks. GN can be easily implemented by a few lines of code in modern libraries.},
archivePrefix = {arXiv},
arxivId = {1803.08494},
author = {Wu, Yuxin and He, Kaiming},
doi = {10.1007/978-3-030-01261-8_1},
eprint = {1803.08494},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wu, He - 2018 - Group normalization.pdf:pdf},
isbn = {9783030012601},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {3--19},
pmid = {4520227},
title = {{Group normalization}},
volume = {11217 LNCS},
year = {2018}
}
@article{Jonsson2018,
author = {Jonsson, Nicklas},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Jonsson, Jonsson - 2018 - Ways to use Machine Learning approaches for software development.pdf:pdf},
title = {{Ways to use Machine Learning approaches for software development}},
year = {2018}
}
@article{LeCun1989,
abstract = {Two novel methods for achieving handwritten digit recognition are described. The first method is based on a neural network chip that performs line thinning and feature extraction using local template matching. The second method is implemented on a digital signal processor and makes extensive use of constrained automatic learning. Experimental results obtained using isolated handwritten digits taken from postal zip codes, a rather difficult data set, are reported and discussed.},
author = {Guyon, I. and {Le Cun}, Y. and Howard, R.E. and Jackel, L.D. and Denker, J.S. and Graf, H.P. and Henderson, D. and Boser, B. and Hubbard, W.},
doi = {10.1109/35.41400},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Guyon et al. - 2002 - Handwritten digit recognition applications of neural network chips and automatic learning.pdf:pdf},
isbn = {0163-6804},
issn = {0163-6804},
journal = {IEEE Communications Magazine},
number = {11},
pages = {41--46},
title = {{Handwritten digit recognition: applications of neural network chips and automatic learning}},
url = {http://ieeexplore.ieee.org/document/41400/},
volume = {27},
year = {2002}
}
@article{Jelenkor2018,
author = {Jelenkor},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Dezs et al. - Unknown - Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat.pdf:pdf},
title = {{Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat 1812}},
year = {2018}
}
@article{Dhollander,
author = {Dhollander, Thijs and Imms, Phoebe},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Dhollander, Imms - Unknown - Fixel-based Analysis of Diffusion MRI Methods , Applications , Challenges and Opportunities.pdf:pdf},
pages = {1--36},
title = {{Fixel-based Analysis of Diffusion MRI : Methods , Applications , Challenges and Opportunities}}
}
@article{Laves2020,
abstract = {Uncertainty quantification in inverse medical imaging tasks with deep learning has received little attention. However, deep models trained on large data sets tend to hallucinate and create artifacts in the reconstructed output that are not anatomically present. We use a randomly initialized convolutional network as parameterization of the reconstructed image and perform gradient descent to match the observation, which is known as deep image prior. In this case, the reconstruction does not suffer from hallucinations as no prior training is performed. We extend this to a Bayesian approach with Monte Carlo dropout to quantify both aleatoric and epistemic uncertainty. The presented method is evaluated on the task of denoising different medical imaging modalities. The experimental results show that our approach yields well-calibrated uncertainty. That is, the predictive uncertainty correlates with the predictive error. This allows for reliable uncertainty estimates and can tackle the problem of hallucinations and artifacts in inverse medical imaging tasks.},
archivePrefix = {arXiv},
arxivId = {2008.08837},
author = {Laves, Max Heinrich and T{\"{o}}lle, Malte and Ortmaier, Tobias},
doi = {10.1007/978-3-030-60365-6_9},
eprint = {2008.08837},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Laves, T{\"{o}}lle, Ortmaier - 2020 - Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior.pdf:pdf},
isbn = {9783030603649},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Deep learning,Hallucination,Variational inference},
pages = {81--96},
title = {{Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior}},
volume = {12443 LNCS},
year = {2020}
}
@article{Arjovsky2017,
abstract = {We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to other distances between distributions.},
archivePrefix = {arXiv},
arxivId = {1701.07875},
author = {Arjovsky, Martin and Chintala, Soumith and Bottou, L{\'{e}}on},
eprint = {1701.07875},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Arjovsky, Chintala, Bottou - 2017 - Wasserstein GAN.pdf:pdf},
title = {{Wasserstein GAN}},
url = {http://arxiv.org/abs/1701.07875},
year = {2017}
}
@article{Zhao2020,
author = {Zhao, Allan and Xu, J I E and Konakovi{\'{c}}-lukovi{\'{c}}, Mina and Hughes, Josephine and Spielberg, Andrew},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Zhao et al. - 2020 - RoboGrammar Graph Grammar for Terrain-Optimized Robot Design.pdf:pdf},
number = {6},
title = {{RoboGrammar : Graph Grammar for Terrain-Optimized Robot Design}},
volume = {39},
year = {2020}
}
@article{Valstar2020,
author = {Valstar, Matthijs H and Bakker, Bernadette S De and Steenbakkers, Roel J H M and Jong, Kees H De and Smit, Laura A and Klein, Thomas J W and Es, Robert J J Van and Hofland, Ingrid and Keizer, Bart De and Jasperse, Bas and Balm, Alfons J M and Schaaf, Arjen Van Der and Langendijk, Johannes A and Smeele, Ludi E and Vogel, Wouter V},
doi = {10.1016/j.radonc.2020.09.034},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Valstar et al. - 2020 - The tubarial salivary glands A potential new organ at risk for radiotherapy.pdf:pdf},
issn = {0167-8140},
journal = {Radiotherapy and Oncology},
number = {xxxx},
pages = {1--7},
publisher = {The Authors},
title = {{The tubarial salivary glands : A potential new organ at risk for radiotherapy}},
url = {https://doi.org/10.1016/j.radonc.2020.09.034},
year = {2020}
}
@article{Durey2016,
author = {Durey, Michael},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Durey - 2016 - Exploration at the edge.pdf:pdf},
number = {2},
pages = {3--40},
title = {{Exploration at the edge}},
volume = {30},
year = {2016}
}
@article{Lu2017,
abstract = {The expressive power of neural networks is important for understanding deep learning. Most existing works consider this problem from the view of the depth of a network. In this paper, we study how width affects the expressiveness of neural networks. Classical results state that depth-bounded (e.g. depth-2) networks with suitable activation functions are universal approximators. We show a universal approximation theorem for width-bounded ReLU networks: width-(n + 4) ReLU networks, where n is the input dimension, are universal approximators. Moreover, except for a measure zero set, all functions cannot be approximated by width-n ReLU networks, which exhibits a phase transition. Several recent works demonstrate the benefits of depth by proving the depth-efficiency of neural networks. That is, there are classes of deep networks which cannot be realized by any shallow network whose size is no more than an exponential bound. Here we pose the dual question on the width-efficiency of ReLU networks: Are there wide networks that cannot be realized by narrow networks whose size is not substantially larger? We show that there exist classes of wide networks which cannot be realized by any narrow network whose depth is no more than a polynomial bound. On the other hand, we demonstrate by extensive experiments that narrow networks whose size exceed the polynomial bound by a constant factor can approximate wide and shallow network with high accuracy. Our results provide more comprehensive evidence that depth may be more effective than width for the expressiveness of ReLU networks.},
archivePrefix = {arXiv},
arxivId = {1709.02540},
author = {Lu, Zhou and Pu, Hongming and Wang, Feicheng and Hu, Zhiqiang and Wang, Liwei},
eprint = {1709.02540},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lu et al. - 2017 - The expressive power of neural networks A view from the width.pdf:pdf},
issn = {10495258},
journal = {Advances in Neural Information Processing Systems},
number = {Nips},
pages = {6232--6240},
title = {{The expressive power of neural networks: A view from the width}},
volume = {2017-Decem},
year = {2017}
}
@article{Beliy2019,
abstract = {Reconstructing observed images from fMRI brain recordings is challenging. Unfortunately, acquiring sufficient "labeled" pairs of {Image, fMRI} (i.e., images with their corresponding fMRI responses) to span the huge space of natural images is prohibitive for many reasons. We present a novel approach which, in addition to the scarce labeled data (training pairs), allows to train fMRI-to-image reconstruction networks also on "unlabeled" data (i.e., images without fMRI recording, and fMRI recording without images). The proposed model utilizes both an Encoder network (image-to-fMRI) and a Decoder network (fMRI-to-image). Concatenating these two networks back-to-back (Encoder-Decoder & Decoder-Encoder) allows augmenting the training with both types of unlabeled data. Importantly, it allows training on the unlabeled test-fMRI data. This self-supervision adapts the reconstruction network to the new input test-data, despite its deviation from the statistics of the scarce training data.},
archivePrefix = {arXiv},
arxivId = {1907.02431},
author = {Beliy, Roman and Gaziv, Guy and Hoogi, Assaf and Strappini, Francesca and Golan, Tal and Irani, Michal},
eprint = {1907.02431},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Beliy et al. - 2019 - From voxels to pixels and back Self-supervision in natural-image reconstruction from fMRI.pdf:pdf},
title = {{From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI}},
url = {http://arxiv.org/abs/1907.02431},
year = {2019}
}
@article{Cocosoc2002,
author = {Cocosoc, Chris A and Kollokian, V and Kwan, Remi K S and Pike, G Bruce and Evans, Alan C},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Cocosoc et al. - 2002 - BrainWebOnline Interface to a 3D MRI Simulated Brain Database.pdf:pdf},
journal = {Hbm},
pages = {1},
title = {{BrainWeb:Online Interface to a 3D MRI Simulated Brain Database}},
url = {http://www.bic.mni.mcgill.ca/users/crisco/HBM97_abs/HBM97_abs.pdf%0Ahttp://www.bic.mni.mcgill.ca/users/crisco/HBM97_poster/HBM97_poster.pdf},
volume = {1131},
year = {2002}
}
@misc{,
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Unknown - Unknown - jackie-robinson-spike-lee-script.pdf.pdf:pdf},
title = {jackie-robinson-spike-lee-script.pdf}
}
@article{Bishop1995,
abstract = {It is well known that the addition of noise to the input data of a neural network during training can, in some circumstances, lead to significant improvements in generalization performance. Previous work has shown that such training with noise is equivalent to a form of regularization in which an extra term is added to the error function. However, the regularization term, which involves second derivatives of the error function, is not bounded below, and so can lead to difficulties if used directly in a learning algorithm based on error minimization. In this paper we show that for the purposes of network training, the regularization term can be reduced to a positive semi-definite form that involves only first derivatives of the network mapping. For a sum-of-squares error function, the regularization term belongs to the class of generalized Tikhonov regularizers. Direct minimization of the regularized error function provides a practical alternative to training with noise.},
author = {Bishop, Chris M.},
doi = {10.1162/neco.1995.7.1.108},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Bishop - 1995 - Training with Noise is Equivalent to Tikhonov Regularization.pdf:pdf},
issn = {0899-7667},
journal = {Neural Computation},
number = {1},
pages = {108--116},
title = {{Training with Noise is Equivalent to Tikhonov Regularization}},
volume = {7},
year = {1995}
}
@article{Mohiaddin1994,
author = {Mohiaddin, R. H.},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mohiaddin - 1994 - Magnetic resonance imaging of single lung transplantation.pdf:pdf},
issn = {03907740},
journal = {Rays - International Journal of Radiological Sciences},
number = {3},
pages = {405--414},
pmid = {7800846},
title = {{Magnetic resonance imaging of single lung transplantation}},
volume = {19},
year = {1994}
}
@book{Han2020,
author = {Han, Shuo and Prince, Jerry L. and Carass, Aaron},
doi = {10.1007/978-3-030-59861-7_41},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Han, Prince, Carass - 2020 - Inhomogeneity Correction in Magnetic Resonance Images Using Deep Image Priors.pdf:pdf},
isbn = {9783030598600},
issn = {16113349},
number = {1},
pages = {404--413},
publisher = {Springer International Publishing},
title = {{Inhomogeneity Correction in Magnetic Resonance Images Using Deep Image Priors}},
url = {http://dx.doi.org/10.1007/978-3-030-59861-7_41},
year = {2020}
}
@article{Peng2020,
abstract = {Background and purpose: To investigate the feasibility of synthesizing computed tomography (CT) images from magnetic resonance (MR) images using generative adversarial networks (GANs) for nasopharyngeal carcinoma (NPC) intensity-modulated radiotherapy (IMRT) planning. Materials and methods: Conventional T1-weighted MR images and CT images were acquired from 173 NPC patients. The MR and CT images of 28 patients were randomly chosen as the independent tested set. The remaining images were used to build a conditional GAN (cGAN) and a cycle-consistency GAN (cycleGAN). A U-net was used as the generator in cGAN, whereas a residual-Unet was used as the generator in cycleGAN. The cGAN was trained using the deformable registered MR-CT image pairs, whereas the cycleGAN was trained using the unregistered MR and CT images. The generated synthetic CT (SCT) images from cGAN and cycleGAN were compared with the true CT images with respect to their Hounsfield Unit (HU) discrepancy and dosimetric accuracy for NPC IMRT plans. Results: The mean absolute errors within the body were 69.67 ± 9.27 HU and 100.62 ± 7.39 HU for the cGAN and cycleGAN, respectively. The 2%/2-mm $\gamma$ passing rates were (98.68 ± 0.94)% and (98.52 ± 1.13)% for the cGAN and cycleGAN, respectively. Meanwhile, the absolute dose discrepancies within the regions of interest were (0.49 ± 0.24)% and (0.62 ± 0.36)%, respectively. Conclusion: Both cGAN and cycleGAN could swiftly generate accurate SCT volume images from MR images, with high dosimetric accuracy for NPC IMRT planning. cGAN was preferable if high-quality MR-CT image pairs were available.},
author = {Peng, Yinglin and Chen, Shupeng and Qin, An and Chen, Meining and Gao, Xingwang and Liu, Yimei and Miao, Jingjing and Gu, Huikuan and Zhao, Chong and Deng, Xiaowu and Qi, Zhenyu},
doi = {10.1016/j.radonc.2020.06.049},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Peng et al. - 2020 - Magnetic resonance-based synthetic computed tomography images generated using generative adversarial networks for n.pdf:pdf},
issn = {18790887},
journal = {Radiotherapy and Oncology},
keywords = {Conditional GAN,Cycle GAN,Generative adversarial networks,MRI-only radiotherapy,Nasopharyngeal carcinoma,Synthetic CT},
pages = {217--224},
pmid = {32622781},
publisher = {The Author(s)},
title = {{Magnetic resonance-based synthetic computed tomography images generated using generative adversarial networks for nasopharyngeal carcinoma radiotherapy treatment planning}},
url = {https://doi.org/10.1016/j.radonc.2020.06.049},
volume = {150},
year = {2020}
}
@article{Choi2018,
abstract = {Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Such a unified model architecture of StarGAN allows simultaneous training of multiple datasets with different domains within a single network. This leads to StarGAN's superior quality of translated images compared to existing models as well as the novel capability of flexibly translating an input image to any desired target domain. We empirically demonstrate the effectiveness of our approach on a facial attribute transfer and a facial expression synthesis tasks.},
archivePrefix = {arXiv},
arxivId = {1711.09020},
author = {Choi, Yunjey and Choi, Minje and Kim, Munyoung and Ha, Jung Woo and Kim, Sunghun and Choo, Jaegul},
doi = {10.1109/CVPR.2018.00916},
eprint = {1711.09020},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Choi et al. - 2018 - StarGAN Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation.pdf:pdf},
isbn = {9781538664209},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {8789--8797},
title = {{StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation}},
year = {2018}
}
@article{VanVeen2018,
abstract = {We propose a novel method for compressed sensing recovery using untrained deep generative models. Our method is based on the recently proposed Deep Image Prior (DIP), wherein the convolutional weights of the network are optimized to match the observed measurements. We show that this approach can be applied to solve any differentiable linear inverse problem, outperforming previous unlearned methods. Unlike various learned approaches based on generative models, our method does not require pre-training over large datasets. We further introduce a novel learned regularization technique, which incorporates prior information on the network weights. This reduces reconstruction error, especially for noisy measurements. Finally, we prove that single-layer DIP networks with constant fraction over-parameterization will perfectly fit any signal through gradient descent, despite being a non-convex problem. This theoretical result provides justification for early stopping.},
archivePrefix = {arXiv},
arxivId = {1806.06438},
author = {{Van Veen}, Dave and Jalal, Ajil and Soltanolkotabi, Mahdi and Price, Eric and Vishwanath, Sriram and Dimakis, Alexandros G.},
eprint = {1806.06438},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Van Veen et al. - 2018 - Compressed Sensing with Deep Image Prior and Learned Regularization.pdf:pdf},
title = {{Compressed Sensing with Deep Image Prior and Learned Regularization}},
url = {http://arxiv.org/abs/1806.06438},
year = {2018}
}
@article{Adamson2014,
abstract = {A sample of 1.53$\times$10$^{9}$ cosmic-ray-induced single muon events has been recorded at 225 meters-water-equivalent using the MINOS Near Detector. The underground muon rate is observed to be highly correlated with the effective atmospheric temperature. The coefficient $\alpha_{T}$, relating the change in the muon rate to the change in the vertical effective temperature, is determined to be 0.428$\pm$0.003(stat.)$\pm$0.059(syst.). An alternative description is provided by the weighted effective temperature, introduced to account for the differences in the temperature profile and muon flux as a function of zenith angle. Using the latter estimation of temperature, the coefficient is determined to be 0.352$\pm$0.003(stat.)$\pm$0.046(syst.).},
author = {Ulyanov, Dmitry and Vedaldi, Andrea and Lempitsky, Victor},
doi = {10.1103/PhysRevD.90.012010},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ulyanov, Vedaldi, Lempitsky - 2016 - Instance Normalization The Missing Ingredient for Fast Stylization.pdf:pdf},
issn = {15502368},
journal = {Physical Review D - Particles, Fields, Gravitation and Cosmology},
number = {1},
pmid = {274002800004},
title = {{Instance Normalization: The Missing Ingredient for Fast Stylization Dmitry}},
url = {http://arxiv.org/abs/1607.08022},
volume = {90},
year = {2014}
}
@article{Ilse2019,
abstract = {We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We propose the domain invariant VAE (DIVA), a generative model that tackles this problem by learning three independent latent subspaces, one for the class, one for the domain and one for the object itself. In addition, we highlight that due to the generative nature of our model we can also incorporate unlabeled data from known or previously unseen domains. This property is highly desirable in fields like medical imaging where labeled data is scarce. We experimentally evaluate our model on the rotated MNIST benchmark where we show that (i) the learned subspaces are indeed complementary to each other, (ii) we improve upon recent works on this task and (iii) incorporating unlabelled data can boost the performance even further.},
archivePrefix = {arXiv},
arxivId = {1905.10427},
author = {Ilse, Maximilian and Tomczak, Jakub M. and Louizos, Christos and Welling, Max},
eprint = {1905.10427},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ilse et al. - 2019 - DIVA Domain invariant variational autoencoder.pdf:pdf},
journal = {Deep Generative Models for Highly Structured Data, DGS@ICLR 2019 Workshop},
keywords = {domain generalization,generative model,invariance,representation learning},
pages = {1--27},
title = {{DIVA: Domain invariant variational autoencoder}},
year = {2019}
}
@article{Jelenkor,
author = {Jelenkor},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Jelenkor - Unknown - Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat 1909.pdf:pdf},
title = {{Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat 1909}}
}
@article{Isensee2019,
abstract = {In this paper we demonstrate the effectiveness of a well trained U-Net in the context of the BraTS 2018 challenge. This endeavour is particularly interesting given that researchers are currently besting each other with architectural modifications that are intended to improve the segmentation performance. We instead focus on the training process arguing that a well trained U-Net is hard to beat. Our baseline U-Net, which has only minor modifications and is trained with a large patch size and a Dice loss function indeed achieved competitive Dice scores on the BraTS2018 validation data. By incorporating additional measures such as region based training, additional training data, a simple postprocessing technique and a combination of loss functions, we obtain Dice scores of 77.88, 87.81 and 80.62, and Hausdorff Distances (95th percentile) of 2.90, 6.03 and 5.08 for the enhancing tumor, whole tumor and tumor core, respectively on the test data. This setup achieved rank two in BraTS2018, with more than 60 teams participating in the challenge.},
archivePrefix = {arXiv},
arxivId = {arXiv:1809.10483v2},
author = {Isensee, Fabian and Kickingereder, Philipp and Wick, Wolfgang and Bendszus, Martin and Maier-Hein, Klaus H.},
doi = {10.1007/978-3-030-11726-9_21},
eprint = {arXiv:1809.10483v2},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Isensee et al. - 2019 - No new-net.pdf:pdf},
isbn = {9783030117252},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Brain tumor,CNN,Dice loss,Glioblastoma,U-Net},
pages = {234--244},
title = {{No new-net}},
volume = {11384 LNCS},
year = {2019}
}
@article{Song2017,
author = {Song, Shuang and Zheng, Yuanjie and He, Yunlong},
doi = {10.18103/bme.v3i1.1550},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Song, Zheng, He - 2017 - A review of Methods for Bias Correction in Medical Images.pdf:pdf},
issn = {23759143},
journal = {Biomedical Engineering Review},
keywords = {bias correction,intensity inhomogeneity,intensity nonuniformity,medial images},
number = {1},
pages = {1--10},
title = {{A review of Methods for Bias Correction in Medical Images}},
volume = {3},
year = {2017}
}
@article{Johnstone2018,
abstract = {Magnetic resonance imaging (MRI) offers superior soft-tissue contrast as compared with computed tomography (CT), which is conventionally used for radiation therapy treatment planning (RTP) and patient positioning verification, resulting in improved target definition. The 2 modalities are co-registered for RTP; however, this introduces a systematic error. Implementing an MRI-only radiation therapy workflow would be advantageous because this error would be eliminated, the patient pathway simplified, and patient dose reduced. Unlike CT, in MRI there is no direct relationship between signal intensity and electron density; however, various methodologies for MRI-only RTP have been reported. A systematic review of these methods was undertaken. The PRISMA guidelines were followed. Embase and Medline databases were searched (1996 to March, 2017) for studies that generated synthetic CT scans (sCT)s for MRI-only radiation therapy. Sixty-one articles met the inclusion criteria. This review showed that MRI-only RTP techniques could be grouped into 3 categories: (1) bulk density override; (2) atlas-based; and (3) voxel-based techniques, which all produce an sCT scan from MR images. Bulk density override techniques either used a single homogeneous or multiple tissue override. The former produced large dosimetric errors (>2%) in some cases and the latter frequently required manual bone contouring. Atlas-based techniques used both single and multiple atlases and included methods incorporating pattern recognition techniques. Clinically acceptable sCTs were reported, but atypical anatomy led to erroneous results in some cases. Voxel-based techniques included methods using routine and specialized MRI sequences, namely ultra-short echo time imaging. High-quality sCTs were produced; however, use of multiple sequences led to long scanning times increasing the chances of patient movement. Using nonroutine sequences would currently be problematic in most radiation therapy centers. Atlas-based and voxel-based techniques were found to be the most clinically useful methods, with some studies reporting dosimetric differences of <1% between planning on the sCT and CT and <1-mm deviations when using sCTs for positional verification.},
author = {Johnstone, Emily and Wyatt, Jonathan J. and Henry, Ann M. and Short, Susan C. and Sebag-Montefiore, David and Murray, Louise and Kelly, Charles G. and McCallum, Hazel M. and Speight, Richard},
doi = {10.1016/j.ijrobp.2017.08.043},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Johnstone et al. - 2018 - Systematic Review of Synthetic Computed Tomography Generation Methodologies for Use in Magnetic Resonance Imag.pdf:pdf},
issn = {1879355X},
journal = {International Journal of Radiation Oncology Biology Physics},
number = {1},
pages = {199--217},
publisher = {Elsevier Inc.},
title = {{Systematic Review of Synthetic Computed Tomography Generation Methodologies for Use in Magnetic Resonance Imaging–Only Radiation Therapy}},
url = {https://doi.org/10.1016/j.ijrobp.2017.08.043},
volume = {100},
year = {2018}
}
@article{Richard,
author = {Jelenkor},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Rich{\'{a}}rd - Unknown - Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat.pdf:pdf},
title = {{Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat 1902}},
year = {2019}
}
@article{Collins1998,
abstract = {Abstract—After conception and implementation of any new medical image processing algorithm, validation is an important step to ensure that the procedure fulfills all requirements set forth at the initial design stage. Although the algorithm must be evaluated on real data, a comprehensive validation requires the additional use of simulated data since it is impossible to establish ground truth with in vivo data. Experiments with simulated data permit controlled evaluation over a wide range of conditions (e.g., different levels of noise, contrast, intensity artefacts, or geometric distortion). Such considerations have become increasingly impor- tant with the rapid growth of neuroimaging, i.e., computational analysis of brain structure and function using brain scanning methods such as positron emission tomography and magnetic resonance imaging. Since simple objects such as ellipsoids or parallelepipedes do not reflect the complexity of natural brain anatomy, we present the design and creation of a realistic, high-resolution, digital, volumetric phantom of the human brain. This three-dimensional digital brain phantom is made up of ten volumetric data sets that define the spatial distribution for different tissues (e.g., grey matter, white matter, muscle, skin, etc.), where voxel intensity is proportional to the fraction of tissue within the voxel. The digital brain phantom can be used to simulate tomographic images of the head. Since the contribution of each tissue type to each voxel in the brain phantom is known, it can be used as the gold standard to test analysis algorithms such as classification procedures which seek to identify the tissue “type” of each image voxel. Furthermore, since the same anatomical phantom may be used to drive simulators for different modalities, it is the ideal tool to test intermodality registration algorithms. The brain phantom and simulated MR images have been made publicly available on the Internet (http://www.bic.mni.mcgill.ca/brainweb).},
author = {Collins, D L and Zijdenbos, A P and Kollokian, V and Sled, J G and Kabani, N J and Holmes, C J and Evans, A C},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Collins et al. - 1998 - Digital Brain Phantom.pdf:pdf},
journal = {Ieee Transactions on Medical Imaging},
keywords = {Brain phantom,magnetic resonance imaging,positron emission tomography,simulation,testing,validation.},
number = {3},
pages = {463--468},
title = {{Digital Brain Phantom}},
volume = {17},
year = {1998}
}
@article{Selvaraju2017,
abstract = {We propose a technique for producing "visual explanations" for decisions from a large class of CNN-based models, making them more transparent. Our approach - Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept, flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Unlike previous approaches, GradCAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers (e.g. VGG), (2) CNNs used for structured outputs (e.g. captioning), (3) CNNs used in tasks with multimodal inputs (e.g. VQA) or reinforcement learning, without any architectural changes or re-training. We combine GradCAM with fine-grained visualizations to create a high-resolution class-discriminative visualization and apply it to off-the-shelf image classification, captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into their failure modes (showing that seemingly unreasonable predictions have reasonable explanations), (b) are robust to adversarial images, (c) outperform previous methods on weakly-supervised localization, (d) are more faithful to the underlying model and (e) help achieve generalization by identifying dataset bias. For captioning and VQA, our visualizations show that even non-attention based models can localize inputs. Finally, we conduct human studies to measure if GradCAM explanations help users establish trust in predictions from deep networks and show that GradCAM helps untrained users successfully discern a "stronger" deep network from a "weaker" one. Our code is available at https://github.com/ramprs/grad-cam. A demo and a video of the demo can be found at http://gradcam.cloudcv.org and youtu.be/COjUB9Izk6E.},
archivePrefix = {arXiv},
arxivId = {arXiv:1610.02391v3},
author = {Selvaraju, Ramprasaath R. and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
doi = {10.1109/ICCV.2017.74},
eprint = {arXiv:1610.02391v3},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Selvaraju et al. - 2017 - Grad-CAM Visual Explanations from Deep Networks via Gradient-Based Localization.pdf:pdf},
isbn = {9781538610329},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {618--626},
title = {{Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization}},
volume = {2017-Octob},
year = {2017}
}
@book{Goodfellow2014,
author = {Goodfellow, Ian J. and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
booktitle = {MIT Press},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Green, Horel, Papachristos - 2017 - Modeling contagion through social networks to explain and predict gunshot violence in Chicago, 2006.pdf:pdf},
title = {{Generative Adversarial Nets}},
year = {2014}
}
@article{Dawant1993,
abstract = {A number of supervised and unsupervised pattern recognition techniques have been proposed in recent years for the segmentation and the quantitative analysis of MR images. However, the efficacy of these techniques is affected by acquisition artifacts such as inter-slice, intra-slice, and inter-patient intensity variations. Here a new approach to the correction of intra-slice intensity variations is presented. Results demonstrate that the correction process enhances the performance of backpropagation neural network classifiers designed for the segmentation of the images. Two slightly different versions of the method are presented. The first version fits an intensity correction surface directly to reference points selected by the user in the images. The second version fits the surface to reference points obtained by an intermediate classification operation. Qualitative and quantitative evaluation of both methods reveals that the first one leads to a better correction of the images than the second but that it is more sensitive to operator errors.},
author = {Dawant, Benoit M. and Zijdenbos, Alex P. and Margolin, Richard A.},
doi = {10.1109/42.251128},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Dawant, Zijdenbos, Margolin - 1993 - Correction of Intensity Variations in MR Images for Computer-Aided Tissue Classification.pdf:pdf},
issn = {1558254X},
journal = {IEEE Transactions on Medical Imaging},
number = {4},
pages = {770--781},
title = {{Correction of Intensity Variations in MR Images for Computer-Aided Tissue Classification}},
volume = {12},
year = {1993}
}
@article{Sled1998,
author = {Sled, J.G. and Zijdenbos, A.P. and Evans, A.C.},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Sled, Zijdenbos, Evans - 1998 - A nonparametric method for automatic correction of intensity nonuniformity in MRI data(2).pdf:pdf},
journal = {IEEE Transactions on Medical Imaging},
number = {1},
pages = {87--97},
title = {{A nonparametric method for automatic correction of intensity nonuniformity in MRI data}},
volume = {17},
year = {1998}
}
@article{Sagel2020,
abstract = {The Deep Image Prior has been recently introduced to solve inverse problems in image processing with no need for training data other than the image itself. However, the original training algorithm of the Deep Image Prior constrains the reconstructed image to be on a manifold described by a convolutional neural network. For some problems, this neglects prior knowledge and can render certain regularizers ineffective. This work proposes an alternative approach that relaxes this constraint and fully exploits all prior knowledge. We evaluate our algorithm on the problem of reconstructing a high-resolution image from a downsampled version and observe a significant improvement over the original Deep Image Prior algorithm.},
author = {Sagel, Alexander and Roumy, Aline and Guillemot, Christine},
doi = {10.1109/ICASSP40776.2020.9054270},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Sagel, Roumy, Guillemot - 2020 - Sub-Dip Optimization on A Subspace with Deep Image Prior Regularization and Application to Superresolut.pdf:pdf},
isbn = {9781509066315},
issn = {15206149},
journal = {ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings},
keywords = {Image reconstruction,Image restoration,Inverse problems,Neural networks},
number = {May},
pages = {2513--2517},
title = {{Sub-Dip: Optimization on A Subspace with Deep Image Prior Regularization and Application to Superresolution}},
volume = {2020-May},
year = {2020}
}
@article{Liu2018,
abstract = {Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value). This often leads to artifacts such as color discrepancy and blurriness. Post-processing is usually used to reduce such artifacts, but are expensive and may fail. We propose the use of partial convolutions, where the convolution is masked and renormalized to be conditioned on only valid pixels. We further include a mechanism to automatically generate an updated mask for the next layer as part of the forward pass. Our model outperforms other methods for irregular masks. We show qualitative and quantitative comparisons with other methods to validate our approach.},
archivePrefix = {arXiv},
arxivId = {arXiv:1804.07723v2},
author = {Liu, Guilin and Reda, Fitsum A. and Shih, Kevin J. and Wang, Ting Chun and Tao, Andrew and Catanzaro, Bryan},
doi = {10.1007/978-3-030-01252-6_6},
eprint = {arXiv:1804.07723v2},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Liu et al. - 2018 - Image Inpainting for Irregular Holes Using Partial Convolutions.pdf:pdf},
isbn = {9783030012519},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Image inpainting,Partial convolution},
pages = {89--105},
title = {{Image Inpainting for Irregular Holes Using Partial Convolutions}},
volume = {11215 LNCS},
year = {2018}
}
@article{Delbracio2015,
abstract = {Numerous recent approaches attempt to remove image blur due to camera shake, either with one or multiple input images, by explicitly solving an inverse and inherently ill-posed deconvolution problem. If the photographer takes a burst of images, a modality available in virtually all modern digital cameras, we show that it is possible to combine them to get a clean sharp version. This is done without explicitly solving any blur estimation and subsequent inverse problem. The proposed algorithm is strikingly simple: it performs a weighted average in the Fourier domain, with weights depending on the Fourier spectrum magnitude. The method can be seen as a generalization of the align and average procedure, with a weighted average, motivated by hand-shake physiology and theoretically supported, taking place in the Fourier domain. The method's rationale is that camera shake has a random nature, and therefore, each image in the burst is generally blurred differently. Experiments with real camera data, and extensive comparisons, show that the proposed Fourier burst accumulation algorithm achieves state-of-the-art results an order of magnitude faster, with simplicity for on-board implementation on camera phones. Finally, we also present experiments in real high dynamic range (HDR) scenes, showing how the method can be straightforwardly extended to HDR photography.},
archivePrefix = {arXiv},
arxivId = {1505.02731},
author = {Delbracio, Mauricio and Sapiro, Guillermo},
doi = {10.1109/TIP.2015.2442914},
eprint = {1505.02731},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Delbracio, Sapiro - 2015 - Removing camera shake via weighted fourier burst accumulation.pdf:pdf},
issn = {10577149},
journal = {IEEE Transactions on Image Processing},
keywords = {burst fusion,camera shake,high dynamic range,low light photography,multi-image deblurring},
number = {11},
pages = {3293--3307},
title = {{Removing camera shake via weighted fourier burst accumulation}},
volume = {24},
year = {2015}
}
@article{Vovk2007,
author = {Vovk, Uro{\v{s}} and Pernu{\v{s}}, Franjo and Likar, Bo{\v{s}}tjan},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Vovk, Pernu{\v{s}}, Likar - 2007 - A review of methods for correction of intensity inhomogeneity in MRI.pdf:pdf},
journal = {IEEE Transactions on Medical Imaging},
number = {3},
pages = {405--421},
title = {{A review of methods for correction of intensity inhomogeneity in MRI}},
volume = {26},
year = {2007}
}
@article{Bergeest,
abstract = {A major problem in magnetic resonance imaging (MRI) is the lack of a pulse sequence dependent standardized intensity scale like the Hounsfield units in computed tomography. This affects the post processing of the acquired images as, in general, segmentation and regis- tration methods depend on the observed image intensities. Different ap- proaches dealing with this problem were proposed recently. In this article we will describe and compare five state-of-the-art standardization methods regarding speed, applicability and accuracy. As a qualitymeasure the mean distance and the Kullback-Leibler divergence are considered. For the experiments 28 MRI head volume images, acquired during clinical routine, were used.},
author = {Bergeest, Jan-Philip and J{\"{a}}ger, Florian},
doi = {10.1007/978-3-540-78640-5_8},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Bergeest, J{\"{a}}ger - 2008 - A Comparison of Five Methods for Signal Intensity Standardization in MRI.pdf:pdf},
isbn = {978-3-540-78640-5},
issn = {1431472X},
journal = {Bildverarbeitung f{\"{u}}r die Medizin 2008},
pages = {36--40},
title = {{A Comparison of Five Methods for Signal Intensity Standardization in MRI}},
url = {http://link.springer.com/10.1007/978-3-540-78640-5_8},
year = {2008}
}
@book{Ross2019,
author = {Ross, Sheldon},
booktitle = {International Perspectives on Publishing Platforms},
doi = {10.4324/9780429491511-1},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ross - 2019 - Simulation.pdf:pdf},
isbn = {9780124158252},
pages = {1--22},
title = {{Simulation}},
year = {2019}
}
@article{Biamonte2017,
abstract = {Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.},
archivePrefix = {arXiv},
arxivId = {1611.09347},
author = {Biamonte, Jacob and Wittek, Peter and Pancotti, Nicola and Rebentrost, Patrick and Wiebe, Nathan and Lloyd, Seth},
doi = {10.1038/nature23474},
eprint = {1611.09347},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Biamonte et al. - 2017 - Quantum machine learning.pdf:pdf},
issn = {14764687},
journal = {Nature},
number = {7671},
pages = {195--202},
pmid = {28905917},
publisher = {Nature Publishing Group},
title = {{Quantum machine learning}},
url = {http://dx.doi.org/10.1038/nature23474},
volume = {549},
year = {2017}
}
@article{Belaroussi2006,
author = {Belaroussi, Boubakeur and Milles, Julien and Carme, Sabin and Zhu, Yue Min and Benoit-Cattin, Hugues},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Belaroussi et al. - 2006 - Intensity non-uniformity correction in MRI Existing methods and their validation.pdf:pdf},
journal = {Medical Image Analysis},
keywords = {Image intensity correction,Intensity non-uniformity artifact,Magnetic resonance imaging,Validation methods},
number = {2},
pages = {234--246},
title = {{Intensity non-uniformity correction in MRI: Existing methods and their validation}},
volume = {10},
year = {2006}
}
@article{Soltanayev2018,
abstract = {Recently developed deep-learning-based denoisers often outperform state-of-the-art conventional denoisers, such as the BM3D. They are typically trained to minimize the mean squared error (MSE) between the output image of a deep neural network and a ground truth image. In deep learning based denoisers, it is important to use high quality noiseless ground truth data for high performance, but it is often challenging or even infeasible to obtain noiseless images in application areas such as hyperspectral remote sensing and medical imaging. In this article, we propose a method based on Stein's unbiased risk estimator (SURE) for training deep neural network denoisers only based on the use of noisy images. We demonstrate that our SURE-based method, without the use of ground truth data, is able to train deep neural network denoisers to yield performances close to those networks trained with ground truth, and to outperform the state-of-the-art denoiser BM3D. Further improvements were achieved when noisy test images were used for training of denoiser networks using our proposed SURE-based method. Code is available at https://github.com/Shakarim94/Net-SURE.},
author = {Soltanayev, Shakarim and Chun, Se Young},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Soltanayev, Chun - 2018 - Training deep learning based denoisers without ground truth data.pdf:pdf},
issn = {10495258},
journal = {Advances in Neural Information Processing Systems},
number = {NeurIPS},
pages = {3257--3267},
title = {{Training deep learning based denoisers without ground truth data}},
volume = {2018-Decem},
year = {2018}
}
@article{Richardson,
archivePrefix = {arXiv},
arxivId = {arXiv:2008.00951v1},
author = {Richardson, Elad and Alaluf, Yuval and Patashnik, Or and Nitzan, Yotam and Azar, Yaniv and Shapiro, Stav and Cohen-or, Daniel and Inversion, Stylegan and Resolution, Super},
eprint = {arXiv:2008.00951v1},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Richardson et al. - Unknown - Encoding in Style a StyleGAN Encoder for Image-to-Image Translation.pdf:pdf},
title = {{Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation}}
}
@article{Loshchilov2011,
abstract = {Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14% and 16.21%, respectively. We also demonstrate its advantages on a dataset of EEG recordings and on a downsampled version of the ImageNet dataset. Our source code is available at https://github.com/loshchil/SGDR},
archivePrefix = {arXiv},
arxivId = {1706.02633},
author = {Loshchilov, Ilya and Hutter, Frank},
doi = {10.1002/fut},
eprint = {1706.02633},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Loshchilov, Hutter - 2016 - SGDR Stochastic Gradient Descent with Warm Restarts.pdf:pdf},
isbn = {1433102730},
issn = {09295593},
number = {3},
pages = {282--306},
pmid = {12046013},
title = {{SGDR: STOCHASTIC GRADIENT DESCENT WITH WARM RESTARTS}},
url = {http://arxiv.org/abs/1608.03983},
volume = {31},
year = {2011}
}
@article{Mnih,
abstract = {We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.},
author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Graves, Alex and Antonoglou, Ioannis and Wierstra, Daan and Riedmiller, Martin},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mnih et al. - Unknown - Nips Deep Q Network (Google).pdf:pdf},
pages = {1--9},
title = {{Nips Deep Q Network (Google)}},
url = {https://www.cs.toronto.edu/$\sim$vmnih/docs/dqn.pdf}
}
@article{Shelley1984,
abstract = {The cornea is one of several orofacial structures requiring glandular secretion for proper lubrication. Glandular secretion is regulated through a neural reflex initiated by trigeminal primary afferent neurons innervating the corneal epithelium. Corneal sensory afferents must respond to irritating and potentially damaging stimuli, as well as drying that occurs with evaporation of the tear film, and the physiological properties of corneal afferents are consistent with these requirements. Polymodal neurons are sensitive to noxious mechanical, thermal and chemical stimuli, mechanoreceptive neurons are selectively activated by mechanical stimuli, and cool cells respond to innocuous cooling. The central terminations of corneal primary afferents are located within two regions of the spinal trigeminal nucleus. The more rostral region, located at the transition between the trigeminal subnucleus caudalis and interpolaris, represents a critical relay for the regulation of the lacrimation reflex. From this region, major control of lacrimation is carried through projections to preganglionic parasympathetic neurons located in or around the superior salivatory nucleus. Dry eye syndrome may be caused by a dysfunction in the tear secreting glands themselves or in the neuronal circuit regulating these glands. Furthermore, the dry eye condition itself may modify the properties of corneal afferents and affect their ability to regulate secretion, a possibility just now being explored.},
author = {Shelley, Mary},
doi = {10.1016/j.exer.2013.08.011},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Shelley - 1984 - Frankenstein.pdf:pdf},
issn = {1096-0007},
pmid = {23994439},
title = {{Frankenstein}},
year = {1984}
}
@article{Kiani2020,
abstract = {A central task in medical imaging is the reconstruction of an image or function from data collected by medical devices (e.g., CT, MRI, and PET scanners). We provide quantum algorithms for image reconstruction with exponential speedup over classical counterparts when data is input as a quantum state. Since outputs of our algorithms are stored in quantum states, individual pixels of reconstructed images may not be efficiently accessed classically; instead, we discuss various methods to extract information from outputs using a variety of quantum post-processing algorithms.},
archivePrefix = {arXiv},
arxivId = {2004.02036},
author = {Kiani, Bobak Toussi and Villanyi, Agnes and Lloyd, Seth},
eprint = {2004.02036},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kiani, Villanyi, Lloyd - 2020 - Quantum Medical Imaging Algorithms.pdf:pdf},
journal = {arXiv},
title = {{Quantum Medical Imaging Algorithms}},
year = {2020}
}
@article{Blumer1989,
abstract = {Valiant's learnability model is extended to learning classes of concepts defined by regions in Euclidean space En. The methods in this paper lead to a unified treatment of some of Valiant's results, along with previous results on distribution-free convergence of certain pattern recognition algorithms. It is shown that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned. Using this parameter, the complexity and closure properties of learnable classes are analyzed, and the necessary and sufficient conditions are provided for feasible learnability. {\textcopyright} 1989, ACM. All rights reserved.},
author = {Blumer, Anselm and Ehrenfeucht, A. and Haussler, David and Warmuth, Manfred K.},
doi = {10.1145/76359.76371},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Blumer et al. - 1989 - Learnability and the Vapnik-Chervonenkis Dimension.pdf:pdf},
issn = {1557735X},
journal = {Journal of the ACM (JACM)},
number = {4},
pages = {929--965},
title = {{Learnability and the Vapnik-Chervonenkis Dimension}},
volume = {36},
year = {1989}
}
@article{Xie2019,
abstract = {Neural networks for image recognition have evolved through extensive manual design from simple chain-like models to structures with multiple wiring paths. The success of ResNets and DenseNets is due in large part to their innovative wiring plans. Now, neural architecture search (NAS) studies are exploring the joint optimization of wiring and operation types, however, the space of possible wirings is constrained and still driven by manual design despite being searched. In this paper, we explore a more diverse set of connectivity patterns through the lens of randomly wired neural networks. To do this, we first define the concept of a stochastic network generator that encapsulates the entire network generation process. Encapsulation provides a unified view of NAS and randomly wired networks. Then, we use three classical random graph models to generate randomly wired graphs for networks. The results are surprising: several variants of these random generators yield network instances that have competitive accuracy on the ImageNet benchmark. These results suggest that new efforts focusing on designing better network generators may lead to new breakthroughs by exploring less constrained search spaces with more room for novel design.},
archivePrefix = {arXiv},
arxivId = {1904.01569},
author = {Xie, Saining and Kirillov, Alexander and Girshick, Ross and He, Kaiming},
eprint = {1904.01569},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Xie et al. - 2019 - Exploring Randomly Wired Neural Networks for Image Recognition.pdf:pdf},
title = {{Exploring Randomly Wired Neural Networks for Image Recognition}},
url = {http://arxiv.org/abs/1904.01569},
year = {2019}
}
@article{Gharbi2017,
abstract = {Performance is a critical challenge in mobile image processing. Given a reference imaging pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements and enable real-time evaluation. For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms. Using pairs of input/output images, we train a convolutional neural network to predict the coefficients of a locally-affine model in bilateral space. Our architecture learns to make local, global, and content-dependent decisions to approximate the desired image transformation. At runtime, the neural network consumes a low-resolution version of the input image, produces a set of affine transformations in bilateral space, upsamples those transformations in an edge-preserving fashion using a new slicing node, and then applies those upsampled transformations to the full-resolution image. Our algorithm processes high-resolution images on a smartphone in milliseconds, provides a real-time viewfinder at 1080p resolution, and matches the quality of state-of-the-art approximation techniques on a large class of image operators. Unlike previous work, our model is trained off-line from data and therefore does not require access to the original operator at runtime. This allows our model to learn complex, scene-dependent transformations for which no reference implementation is available, such as the photographic edits of a human retoucher.},
archivePrefix = {arXiv},
arxivId = {1707.02880},
author = {Gharbi, Micha{\"{e}}l and Chen, Jiawen and Barron, Jonathan T. and Hasinoff, Samuel W. and Durand, Fr{\'{e}}do},
doi = {10.1145/3072959.3073592},
eprint = {1707.02880},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gharbi et al. - 2017 - Deep Bilateral Learning for Real-Time Image Enhancement.pdf:pdf},
issn = {07300301},
number = {4},
pmid = {4876},
title = {{Deep Bilateral Learning for Real-Time Image Enhancement}},
url = {http://arxiv.org/abs/1707.02880%0Ahttp://dx.doi.org/10.1145/3072959.3073592},
volume = {36},
year = {2017}
}
@article{Ronneberger2015,
author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ronneberger, Fischer, Brox - 2015 - U-Net Convolutional Networks for Biomedical Image Segmentation.pdf:pdf},
journal = {Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015},
title = {{U-net: Convolutional networks for biomedical image segmentation}},
year = {2015}
}
@article{Gan2020,
archivePrefix = {arXiv},
arxivId = {arXiv:2003.01217v2},
author = {Gan, M R I Super-resolution and Smaller, Densenet},
eprint = {arXiv:2003.01217v2},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gan, Smaller - 2020 - MRI Super-Resolution with GAN and 3D Multi-Level DenseNet Smaller, Faster, and Better.pdf:pdf},
keywords = {3d convolutional neural,deep learning,super-resolution},
number = {Debiao Li},
title = {{MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better}},
year = {2020}
}
@article{Mason2019,
abstract = {Image quality metrics (IQMs) such as root mean square error (RMSE) and structural similarity index (SSIM) are commonly used in the evaluation and optimization of accelerated magnetic resonance imaging (MRI) acquisition and reconstruction strategies. However, it is unknown how well these indices relate to a radiologist's perception of diagnostic image quality. In this study, we compare the image quality scores of five radiologists with the RMSE, SSIM, and other potentially useful IQMs: peak signal to noise ratio (PSNR) multi-scale SSIM (MSSSIM), information-weighted SSIM (IWSSIM), gradient magnitude similarity deviation (GMSD), feature similarity index (FSIM), high dynamic range visible difference predictor (HDRVDP), noise quality metric (NQM), and visual information fidelity (VIF). The comparison uses a database of MR images of the brain and abdomen that have been retrospectively degraded by noise, blurring, undersampling, motion, and wavelet compression for a total of 414 degraded images. A total of 1017 subjective scores were assigned by five radiologists. IQM performance was measured via the Spearman rank order correlation coefficient (SROCC) and statistically significant differences in the residuals of the IQM scores and radiologists' scores were tested. When considering SROCC calculated from combining scores from all radiologists across all image types, RMSE and SSIM had lower SROCC than six of the other IQMs included in the study (VIF, FSIM, NQM, GMSD, IWSSIM, and HDRVDP). In no case did SSIM have a higher SROCC or significantly smaller residuals than RMSE. These results should be considered when choosing an IQM in future imaging studies.},
author = {Mason, Allister and Rioux, James and Clarke, Sharon E. and Costa, Andreu and Schmidt, Matthias and Keough, Valerie and Huynh, Thien and Beyea, Steven},
doi = {10.1109/tmi.2019.2930338},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mason et al. - 2019 - Comparison of Objective Image Quality Metrics to Expert Radiologists' Scoring of Diagnostic Quality of MR Images.pdf:pdf},
issn = {0278-0062},
journal = {IEEE Transactions on Medical Imaging},
number = {4},
pages = {1064--1072},
pmid = {31535985},
publisher = {IEEE},
title = {{Comparison of Objective Image Quality Metrics to Expert Radiologists' Scoring of Diagnostic Quality of MR Images}},
volume = {39},
year = {2019}
}
@article{Mescheder2018,
abstract = {GAN training for absolutely continuous data and generator distributions. In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent. Furthermore, we discuss reg- ularization strategies that were recently proposed to stabilize GAN training. Our analysis shows that GAN training with instance noise or zero- centered gradient penalties converges. On the other hand, we show that Wasserstein-GANs and WGAN-GP with a finite number of discriminator updates per generator update do not always converge to the equilibrium point. We discuss these results, leading us to a new explanation for the stability problems of GAN training. Based on our analysis, we extend our convergence results to more general GANs and prove local convergence for simplified gradient penalties even if the generator and data distributions lie on lower dimensional manifolds. We find these penalties to work well in practice and use them to learn high- resolution generative image models for a variety of datasets with little hyperparameter tuning.},
archivePrefix = {arXiv},
arxivId = {1801.04406},
author = {Mescheder, Lars and Geiger, Andreas and Nowozin, Sebastian},
eprint = {1801.04406},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mescheder, Geiger, Nowozin - 2018 - Which training methods for GANs do actually converge.pdf:pdf},
isbn = {9781510867963},
journal = {35th International Conference on Machine Learning, ICML 2018},
pages = {5589--5626},
title = {{Which training methods for GANs do actually converge?}},
volume = {8},
year = {2018}
}
@article{Nilsson2019,
author = {Nilsson, Erik},
file = {:C\:/Users/attil/Downloads/Rapport_v2_4.pdf:pdf},
title = {{Super-Resolution for Fast Multi-Contrast Magnetic Resonance Imaging}},
year = {2019}
}
@article{Dou2016,
abstract = {Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelled by radiologists but this procedure is laborious, time-consuming, and error prone. In this paper, we propose a novel automatic method to detect CMBs from magnetic resonance (MR) images by exploiting the 3D convolutional neural network (CNN). Compared with previous methods that employed either low-level hand-crafted descriptors or 2D CNNs, our method can take full advantage of spatial contextual information in MR volumes to extract more representative high-level features for CMBs, and hence achieve a much better detection accuracy. To further improve the detection performance while reducing the computational cost, we propose a cascaded framework under 3D CNNs for the task of CMB detection. We first exploit a 3D fully convolutional network (FCN) strategy to retrieve the candidates with high probabilities of being CMBs, and then apply a well-trained 3D CNN discrimination model to distinguish CMBs from hard mimics. Compared with traditional sliding window strategy, the proposed 3D FCN strategy can remove massive redundant computations and dramatically speed up the detection process. We constructed a large dataset with 320 volumetric MR scans and performed extensive experiments to validate the proposed method, which achieved a high sensitivity of 93.16% with an average number of 2.74 false positives per subject, outperforming previous methods using low-level descriptors or 2D CNNs by a significant margin. The proposed method, in principle, can be adapted to other biomarker detection tasks from volumetric medical data.},
author = {Dou, Qi and Chen, Hao and Yu, Lequan and Zhao, Lei and Qin, Jing and Wang, Defeng and Mok, Vincent C.T. and Shi, Lin and Heng, Pheng Ann},
doi = {10.1109/TMI.2016.2528129},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Dou et al. - 2016 - Automatic Detection of Cerebral Microbleeds from MR Images via 3D Convolutional Neural Networks.pdf:pdf},
isbn = {2013101015},
issn = {1558254X},
journal = {IEEE Transactions on Medical Imaging},
keywords = {3D convolutional neural networks,biomarker detection,cerebral microbleeds,deep learning,susceptibility-weighted imaging},
number = {5},
pages = {1182--1195},
pmid = {26886975},
title = {{Automatic Detection of Cerebral Microbleeds from MR Images via 3D Convolutional Neural Networks}},
volume = {35},
year = {2016}
}
@article{Lewis2004,
abstract = {Longitudinal MR imaging is increasingly being used to measure cerebral atrophy progression in dementia and other neurological disorders. Differences in intensity inhomogeneity between serial scans can confound these measurements. This differential bias also distorts nonlinear registration and makes both manual and automated segmentation of tissue type less reliable. A technique is described for the correction of this differential bias that makes no assumptions about signal distribution, bias field or signal homogeneity. Instead, the bias field calculation is performed on the basis that the remaining structure in the difference image of registered serial scans has small-scale structure. The differential bias field is of much larger scale and can thus be obtained by applying an appropriate filter to the difference image. The serial scan pair is then corrected for the differential bias field and atrophy measurement can be performed on the corrected scan pair. Application of a known, simulated bias field to real serial MR images was shown to alter atrophy measurements significantly. The differential correction method recovered the applied differential bias field and thereby improved atrophy measurements. This method was then applied to serial imaging in patients with dementia using a set of serial scan pairs with visually identified, significant differential bias and a set of scan pairs with negligible differential bias. Differential bias correction specifically reduced the variance of the atrophy measure significantly for the scans with significant differential bias. {\textcopyright} 2004 Elsevier Inc. All rights reserved.},
author = {Lewis, Emma B. and Fox, Nicholas C.},
doi = {10.1016/j.neuroimage.2004.04.030},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lewis, Fox - 2004 - Correction of differential intensity inhomogeneity in longitudinal MR images.pdf:pdf},
issn = {10538119},
journal = {NeuroImage},
keywords = {BBSI,Bias field,Cerebral atrophy,Dementia,Imaging,Intensity inhomogeneity,MRI},
number = {1},
pages = {75--83},
title = {{Correction of differential intensity inhomogeneity in longitudinal MR images}},
volume = {23},
year = {2004}
}
@article{Mirza2014,
abstract = {Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels.},
archivePrefix = {arXiv},
arxivId = {1411.1784},
author = {Mirza, Mehdi and Osindero, Simon},
eprint = {1411.1784},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mirza, Osindero - 2014 - Conditional Generative Adversarial Nets.pdf:pdf},
pages = {1--7},
title = {{Conditional Generative Adversarial Nets}},
url = {http://arxiv.org/abs/1411.1784},
year = {2014}
}
@article{Moyer2020,
abstract = {Purpose: In the present work, we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation. Theory and Methods: Pooled imaging data from multiple sources are subject to variation between the sources. Correcting for these biases has become very important as imaging studies increase in size and multi-site cases become more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory-based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to underlying structures. To implement this, we use a deep learning method based on variational auto-encoders (VAE) to construct scanner invariant encodings of the imaging data. Results: To evaluate our method, we use training data from the 2018 MICCAI Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context. Conclusions: As imaging studies continue to grow, the use of pooled multi-site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data.},
archivePrefix = {arXiv},
arxivId = {1904.05375},
author = {Moyer, Daniel and {Ver Steeg}, Greg and Tax, Chantal M.W. and Thompson, Paul M.},
doi = {10.1002/mrm.28243},
eprint = {1904.05375},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Moyer et al. - 2020 - Scanner invariant representations for diffusion MRI harmonization.pdf:pdf},
issn = {15222594},
journal = {Magnetic Resonance in Medicine},
keywords = {diffusion MRI,harmonization,invariant representation},
number = {4},
pages = {2174--2189},
pmid = {32250475},
title = {{Scanner invariant representations for diffusion MRI harmonization}},
volume = {84},
year = {2020}
}
@article{deng2009,
abstract = {The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called ldquoImageNetrdquo, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.},
author = {Jia, Deng and Wei, Dong and Richard, Socher and Li-Jia, Li and Kai, Li and Li, Fei-Fei},
doi = {10.14842/jpnjnephrol1959.20.1221},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Jia Deng, Wei Dong, Richard Socher, Li-Jia Li - 1978 - ImageNet A Large-Scale Hierarchical Image Database.pdf:pdf},
issn = {03852385},
journal = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
title = {{ImageNet: A Large-Scale Hierarchical Image Database}},
year = {2009}
}
@article{,
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Unknown - 2019 - COMPARISON OF GENERATIVE DEEP MACHINE LEARNING MODELS IN MEDICAL IMAGING APPLICATIONS.pdf:pdf},
title = {{COMPARISON OF GENERATIVE DEEP MACHINE LEARNING MODELS IN MEDICAL IMAGING APPLICATIONS}},
year = {2019}
}
@article{Maspero2018,
abstract = {To enable magnetic resonance (MR)-only radiotherapy and facilitate modelling of radiation attenuation in humans, synthetic CT (sCT) images need to be generated. Considering the application of MR-guided radiotherapy and online adaptive replanning, sCT generation should occur within minutes. This work aims at assessing whether an existing deep learning network can rapidly generate sCT images for accurate MR-based dose calculations in the entire pelvis. A study was conducted on data of 91 patients with prostate (59), rectal (18) and cervical (14) cancer who underwent external beam radiotherapy acquiring both CT and MRI for patients' simulation. Dixon reconstructed water, fat and in-phase images obtained from a conventional dual gradient-recalled echo sequence were used to generate sCT images. A conditional generative adversarial network (cGAN) was trained in a paired fashion on 2D transverse slices of 32 prostate cancer patients. The trained network was tested on the remaining patients to generate sCT images. For 30 patients in the test set, dose recalculations of the clinical plan were performed on sCT images. Dose distributions were evaluated comparing voxel-based dose differences, gamma and dose-volume histogram (DVH) analysis. The sCT generation required 5.6 s and 21 s for a single patient volume on a GPU and CPU, respectively. On average, sCT images resulted in a higher dose to the target of maximum 0.3%. The average gamma pass rates using the 3%, 3 mm and 2%, 2 mm criteria were above 97 and 91%, respectively, for all volumes of interests considered. All DVH points calculated on sCT differed less than ±2.5% from the corresponding points on CT. Results suggest that accurate MR-based dose calculation using sCT images generated with a cGAN trained on prostate cancer patients is feasible for the entire pelvis. The sCT generation was sufficiently fast for integration in an MR-guided radiotherapy workflow.},
archivePrefix = {arXiv},
arxivId = {1802.06468},
author = {Maspero, Matteo and Savenije, Mark H.F. and Dinkla, Anna M. and Seevinck, Peter R. and Intven, Martijn P.W. and Jurgenliemk-Schulz, Ina M. and Kerkmeijer, Linda G.W. and {Van Den Berg}, Cornelis A.T.},
doi = {10.1088/1361-6560/aada6d},
eprint = {1802.06468},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Maspero et al. - 2018 - Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-onl.pdf:pdf},
issn = {13616560},
journal = {Physics in Medicine and Biology},
keywords = {Magnetic resonance imaging,cancer,dose calculations,generative adversarial network,medical imaging,neural network,pseudo-CT},
number = {18},
publisher = {IOP Publishing},
title = {{Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy}},
volume = {63},
year = {2018}
}
@article{Rutegard2019,
author = {Ruteg{\aa}rd, Miriam K. and B{\aa}tsman, Malin and Axelsson, Jan and Brynolfsson, Patrik and Br{\"{a}}nnstr{\"{o}}m, Fredrik and Ruteg{\aa}rd, J{\"{o}}rgen and Ljuslinder, Ingrid and Blomqvist, Lennart and Palmqvist, Richard and Ruteg{\aa}rd, Martin and Riklund, Katrine},
doi = {10.1186/s40644-019-0237-1},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ruteg{\aa}rd et al. - 2019 - PETMRI and PETCT hybrid imaging of rectal cancer – description and initial observations from the RECTOPET (REct.pdf:pdf},
journal = {Cancer Imaging},
keywords = {Rectal neoplasm,Rectal tumour,Staging,Lymph nodes,,ct,fdg-,fdg-pet,lymph nodes,mri,pet,rectal neoplasm,rectal tumour,staging,tumour deposits},
number = {1},
pages = {1--9},
publisher = {Cancer Imaging},
title = {{PET/MRI and PET/CT hybrid imaging of rectal cancer – description and initial observations from the RECTOPET (REctal Cancer trial on PET/MRI/CT) study}},
volume = {19},
year = {2019}
}
@article{Kata2018,
author = {Kata, Kub{\'{i}}nyi and Kata, R{\'{a}}cz},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kata, Kata - 2018 - skandinavisztikai foly{\'{o}}irat 1(3).pdf:pdf},
title = {skandinavisztikai foly{\'{o}}irat 1.},
year = {2018}
}
@article{Brown2017,
abstract = {A typical gait analysis data collection consists of a series of discrete trials, where a participant initiates gait, walks through a motion capture volume, and then terminates gait. This is not a normal ‘everyday' gait pattern, yet measurements are considered representative of normal walking. However, walking speed, a global descriptor of gait quality that can affect joint kinematics and kinetics, may be different during discrete trials, compared to continuous walking. Therefore, the purpose of this study was to investigate the effect of continuous walking versus discrete trials on walking speed and walking speed variability. Data were collected for 25 healthy young adults performing 2 walking tasks. The first task represented a typical gait data collection session, where subjects completed repeated trials, beginning from a standstill and walking along a 12-m walkway. The second task was continuous walking along a “figure-of-8” circuit, with 1 section containing the same 12-m walkway. Walking speed was significantly higher during the discrete trials compared to the continuous trials (p < .001), but there were no significant differences in walking speed variability between the conditions. The results suggest that choice of gait protocol may affect results where variables are sensitive to walking speed.},
author = {Nair, V.},
doi = {10.1123/jab.2016-0355},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Nair - 2017 - Rectified Linear Units Improve Restricted Boltzmann Machines Vinod.pdf:pdf},
issn = {15432688},
journal = {Journal of Applied Biomechanics},
keywords = {Gait,Methodology,Walking speed},
number = {5},
pages = {384--387},
title = {{Rectified Linear Units Improve Restricted Boltzmann Machines Vinod}},
volume = {33},
year = {2017}
}
@article{Miyato2018,
abstract = {One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques. The code with Chainer (Tokui et al., 2015), generated images and pretrained models are available at https://github.com/pfnet-research/sngan_projection.},
archivePrefix = {arXiv},
arxivId = {1802.05957},
author = {Miyato, Takeru and Kataoka, Toshiki and Koyama, Masanori and Yoshida, Yuichi},
eprint = {1802.05957},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Miyato et al. - 2018 - Spectral normalization for generative adversarial networks.pdf:pdf},
journal = {6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings},
title = {{Spectral normalization for generative adversarial networks}},
year = {2018}
}
@article{Snoek2012,
abstract = {Machine learning algorithms frequently require careful tuning of model hyperparameters, regularization terms, and optimization parameters. Unfortunately, this tuning is often a "black art" that requires expert experience, unwritten rules of thumb, or sometimes brute-force search. Much more appealing is the idea of developing automatic approaches which can optimize the performance of a given learning algorithm to the task at hand. In this work, we consider the automatic tuning problem within the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP). The tractable posterior distribution induced by the GP leads to efficient use of the information gathered by previous experiments, enabling optimal choices about what parameters to try next. Here we show how the effects of the Gaussian process prior and the associated inference procedure can have a large impact on the success or failure of Bayesian optimization. We show that thoughtful choices can lead to results that exceed expert-level performance in tuning machine learning algorithms. We also describe new algorithms that take into account the variable cost (duration) of learning experiments and that can leverage the presence of multiple cores for parallel experimentation. We show that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization on a diverse set of contemporary algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks.},
archivePrefix = {arXiv},
arxivId = {1206.2944},
author = {Snoek, Jasper and Larochelle, Hugo and Adams, Ryan P.},
doi = {2012arXiv1206.2944S},
eprint = {1206.2944},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Snoek, Larochelle, Adams - 2012 - Practical Bayesian Optimization of Machine Learning Algorithms.pdf:pdf},
isbn = {9781627480031},
issn = {10495258},
pages = {1--9},
pmid = {9377276},
title = {{Practical Bayesian Optimization of Machine Learning Algorithms}},
url = {http://arxiv.org/abs/1206.2944},
year = {2012}
}
@article{Kramer2007,
abstract = {We analyze and compare the efficiency and accuracy of two simulation methods for homogeneous random fields with multiscale resolution. We consider in particular the Fourier-wavelet method and three variants of the Randomization method: (A) without any stratified sampling of wavenumber space, (B) with stratified sampling of wavenumbers with equal energy subdivision, (C) with stratified sampling with a logarithmically uniform subdivision. We focus primarily on fractal Gaussian random fields with Kolmogorov-type spectra. Previous work has shown that variants (A) and (B) of the Randomization method are only able to generate a self-similar structure function over three to four decades with reasonable computational effort. By contrast, variant (C), along with the Fourier-wavelet method, is able to reproduce accurate self-similar scaling of the structure function over a number of decades increasing linearly with computational effort (for our examples we will show that nine decades can be reproduced). We provide some conceptual and numerical comparison of the various cost contributions to each random field simulation method. We find that when evaluating ensemble-averaged quantities like the correlation and structure functions, as well as some multi-point statistical characteristics, the Randomization method can provide good accuracy with less cost than the Fourier-wavelet method. The cost of the Randomization method relative to the Fourier-wavelet method, however, appears to increase with the complexity of the random field statistics which are to be calculated accurately. Moreover, the Fourier-wavelet method has better ergodic properties, and hence becomes more efficient for the computation of spatial (rather than ensemble) averages which may be important in simulating the solutions to partial differential equations with random field coefficients. {\textcopyright} 2007 Elsevier Inc. All rights reserved.},
author = {Kramer, Peter R. and Kurbanmuradov, Orazgeldi and Sabelfeld, Karl},
doi = {10.1016/j.jcp.2007.05.002},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kramer, Kurbanmuradov, Sabelfeld - 2007 - Comparative analysis of multiscale Gaussian random field simulation algorithms.pdf:pdf},
issn = {00219991},
journal = {Journal of Computational Physics},
keywords = {Fourier wavelet,Monte Carlo,Multiscale,Random field simulation,Randomization},
number = {1},
pages = {897--924},
title = {{Comparative analysis of multiscale Gaussian random field simulation algorithms}},
volume = {226},
year = {2007}
}
@article{Hou2006,
author = {Z., Hou},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hou - 2006 - A review on MR image intensity inhomogeneity correction.pdf:pdf},
journal = {International Journal of Biomedical Imaging},
number = {1},
pages = {1--11},
title = {{A Review on MR Image Intensity Inhomogeneity Correction}},
volume = {2006},
year = {2006}
}
@article{Wicks1993,
author = {Wicks, David A G and Barker, Gareth J. and Tofts, Paul S.},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wicks, Barker, Tofts - 1993 - Correction of intensity nonuniformity in MR images of any orientation.pdf:pdf},
journal = {Magnetic Resonance Imaging},
number = {2},
pages = {183--196},
title = {{Correction of intensity nonuniformity in MR images of any orientation}},
volume = {11},
year = {1993}
}
@article{Jager2006,
abstract = {A major problem of segmentation of magnetic res- onance imaging is that intensities are not standard- ized like in computed tomography. In this article we will present a new method for MRI intensity standardization by aligning histograms of higher di- mensions. So the correction process is independent from spatial coherences and prior segmentations of the reference and newly acquired images. Although the approach is not limited to a speci?c application or area of the body, it is utilized for fast classification of brain tissue. Therefore, reference statistics are computed once using a hidden Markov random field approach. The intensity corrected images are then classified using these learned statistics. In order to evaluate the presented methods the mean differences of intra-patient time series are chosen as reference. Furthermore, results of fast brain tissue classification are presented.},
author = {Jager, F. and Deuerling-Zheng, Y. and Frericks, Bernd and Wacker, Frank and Hornegger, Joachim},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Jager et al. - 2006 - A new method for MRI intensity standardization with application to lesion detection in the brain.pdf:pdf},
journal = {Vision, modeling, and visualization 2006: proceedings, November 22-24, 2006, Aachen, Germany},
pages = {269},
title = {{A new method for MRI intensity standardization with application to lesion detection in the brain}},
url = {http://books.google.com/books?hl=en&lr=&id=zndnSzkfkXwC&oi=fnd&pg=PA269&dq=A+New+Method+for+MRI+Intensity+Standardization+with+Application+to+Lesion+Detection+in+the+Brain&ots=0X84fzPEj2&sig=oo8YFDJMxMpVdkPiXrLGKrNnxck},
year = {2006}
}
@article{Minto2016,
abstract = {Visual odometry has been used in many fields, especially in robotics and intelligent vehicles. Since local descriptors are robust to background clutter, occlusion and other content variations, they have been receiving more and more attention in the application of the detector-descriptor based visual odometry. To our knowledge, however, there is no extensive, comparative evaluation inves-tigating the performance of the detector-descriptor based methods in the sce-nario of monocular visual-IMU (Inertial Measurement Unit) odometry. In this paper, we therefore perform such an evaluation under a unified framework. We select five typical routes from the challenging KITTI dataset by taking into account the length and shape of routes, the impact of independent motions due to other vehicles and pedestrians. In terms of the five routes, we conduct five different experiments in order to assess the performance of different combina-tions of salient point detector and local descriptor in various road scenes, respectively. The results obtained in this study potentially provide a series of guidelines for the selection of salient point detectors and local descriptors.},
archivePrefix = {arXiv},
arxivId = {1603.06937},
author = {Minto, Ludovico and Pagnutti, Giampaolo and Zanuttigh, Pietro},
doi = {10.1007/978-3-319-49409-8_12},
eprint = {1603.06937},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Minto, Pagnutti, Zanuttigh - 2016 - Scene segmentation driven by deep learning and surface fitting.pdf:pdf},
isbn = {9783319494081},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {CNN,Color,Deep learning,Depth,Kinect,NURBS,Segmentation},
pages = {118--132},
pmid = {4520227},
title = {{Scene segmentation driven by deep learning and surface fitting}},
volume = {9915 LNCS},
year = {2016}
}
@book{JaberJuntu2005,
abstract = {Bias field signal is a low-frequency and very smooth signal that corrupts MRI images specially those produced by old MRI (Magnetic Resonance Imaging) machines. Image processing algorithms such as segmentation, texture analysis or classification that use the graylevel values of image pixels will not produce satisfactory results. A pre-processing step is needed to correct for the bias field signal before submitting corrupted MRI images to such algorithms or the algorithms should be modified. In this report we discuss two approaches to deal with bias field corruption. The first approach can be used as a preprocessing step where the corrupted MRI image is restored by dividing it by an estimated bias field signal using a surface fitting approach. The second approach shows how to modify the fuzzy c-means algorithm so that it can be used to segment an MRI image corrupted by a bias field signal.},
author = {Juntu, Jaber and Sijbers, Jan and Dyck, Dirk and Gielen, Jan},
doi = {10.1007/3-540-32390-2_64},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Juntu et al. - 2008 - Bias Field Correction for MRI Images.pdf:pdf},
isbn = {9783540250548},
pages = {543--551},
title = {{Bias Field Correction for MRI Images}},
year = {2008}
}
@article{Cho2018,
abstract = {Intensity-modulated radiation therapy (IMRT) has been considered the most successful development in radiation oncology since the introduction of computed tomography into treatment planning that enabled three-dimensional conformal radiotherapy in 1980s. More than three decades have passed since the concept of inverse planning was first introduced in 1982, and IMRT has become the most important and common modality in radiation therapy. This review will present developments in inverse IMRT treatment planning and IMRT delivery using multileaf collimators, along with the associated key concepts. Other relevant issues and future perspectives are also presented.},
author = {Cho, Byungchul},
doi = {10.3857/roj.2018.00122},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Cho - 2018 - Intensity-modulated radiation therapy A review with a physics perspective.pdf:pdf},
issn = {22343156},
journal = {Radiation Oncology Journal},
keywords = {Intensity-modulated radiation therapy,Quality assurance,Radiotherapy planning,Three-dimensional conformal radiotherapy},
number = {1},
pages = {1--10},
title = {{Intensity-modulated radiation therapy: A review with a physics perspective}},
volume = {36},
year = {2018}
}
@article{Sled1997,
abstract = {{\textcopyright} Springer-Verlag Berlin Heidelberg 1997. Intensity non-uniformity is an artifact often seen in MRI data that can significantly degrade automatic segmentation and prevent quantitative analysis. Using simulated MI$\sim$I data, we have quantitatively compared the performance of three methods of correcting for intensity non-uniformity. In this analysis, novel stereotaxic space techniques were employed so that all three methods were fully automatic and capable of correcting volumes of any pulse sequence. Our results, based on correcting simulated T1, T2, and proton density (PD) weighted MRI scans with various levels of noise and non-uniformity, showed no method to be superior in all categories. However, the N3 technique distinguished itself for uniformly good performance without the limitation of assuming a sophisticated model of the data.},
author = {Sled, John G. and Zijdenbos, Alex P. and Evans, Alan C.},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Sled, Zijdenbos, Evans - 1997 - A comparison of retrospective intensity non-uniformity correction methods for MRI.pdf:pdf},
isbn = {3540630465},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {459--464},
title = {{A comparison of retrospective intensity non-uniformity correction methods for MRI}},
volume = {1230},
year = {1997}
}
@article{Wu2020,
abstract = {Despite success on a wide range of problems related to vision, generative adversarial networks (GANs) often suffer from inferior performance due to unstable training, especially for text generation. To solve this issue, we propose a new variational GAN training framework which enjoys superior training stability. Our approach is inspired by a connection of GANs and reinforcement learning under a variational perspective. The connection leads to (1) probability ratio clipping that regularizes generator training to prevent excessively large updates, and (2) a sample re-weighting mechanism that improves discriminator training by downplaying bad-quality fake samples. Moreover, our variational GAN framework can provably overcome the training issue in many GANs that an optimal discriminator cannot provide any informative gradient to training generator. By plugging the training approach in diverse state-of-the-art GAN architectures, we obtain significantly improved performance over a range of tasks, including text generation, text style transfer, and image generation.},
archivePrefix = {arXiv},
arxivId = {2006.06900},
author = {Wu, Yue and Zhou, Pan and Wilson, Andrew Gordon and Xing, Eric P. and Hu, Zhiting},
eprint = {2006.06900},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wu et al. - 2020 - Improving GAN Training with Probability Ratio Clipping and Sample Reweighting.pdf:pdf},
number = {NeurIPS},
pages = {1--16},
title = {{Improving GAN Training with Probability Ratio Clipping and Sample Reweighting}},
url = {http://arxiv.org/abs/2006.06900},
year = {2020}
}
@article{Richard1923,
author = {Jelenkor},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Unknown - 2019 - Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat 1903.pdf:pdf},
title = {{Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat 1903}},
year = {2019}
}
@article{Kim2020,
abstract = {Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows have used supervised learning, which requires paired data for training. This limitation hinders more practical applications of super-resolution reconstruction. Therefore, we present an unsupervised learning model that adopts a cycle-consistent generative adversarial network that can be trained with unpaired turbulence data for super-resolution reconstruction. Our model is validated using three examples: (i) recovering the original flow field from filtered data using direct numerical simulation (DNS) of homogeneous isotropic turbulence; (ii) reconstructing full-resolution fields using partially measured data from the DNS of turbulent channel flows; and (iii) generating a DNS-resolution flow field from large eddy simulation (LES) data for turbulent channel flows. In examples (i) and (ii), for which paired data are available for supervised learning, our unsupervised model demonstrates qualitatively and quantitatively similar performance as that of the best supervised-learning model. More importantly, in example (iii), where supervised learning is impossible, our model successfully reconstructs the high-resolution flow field of statistical DNS quality from the LES data. This demonstrates that unsupervised learning of turbulence data is indeed possible, opening a new door for the wide application of super-resolution reconstruction of turbulent fields.},
archivePrefix = {arXiv},
arxivId = {2007.15324},
author = {Kim, Hyojin and Kim, Junhyuk and Won, Sungjin and Lee, Changghoon},
eprint = {2007.15324},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kim et al. - 2020 - Unsupervised deep learning for super-resolution reconstruction of turbulence.pdf:pdf},
keywords = {as these must,author during the online,authors should not enter,be chosen by the,cambridge,data,during the typesetting process,jfm-,journals,keywords,keywords on the manuscript,list,org,pdf for the full,relatedlink,see http,submission process and will,then be added},
pages = {1--34},
title = {{Unsupervised deep learning for super-resolution reconstruction of turbulence}},
url = {http://arxiv.org/abs/2007.15324},
year = {2020}
}
@article{Mao,
archivePrefix = {arXiv},
arxivId = {arXiv:1606.08921v3},
author = {Mao, Xiao-jiao and Shen, Chunhua and Yang, Yu-bin},
eprint = {arXiv:1606.08921v3},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mao, Shen, Yang - Unknown - Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections.pdf:pdf},
pages = {1--17},
title = {{Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections}}
}
@article{Isola2017,
abstract = {We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.},
author = {Isola, Phillip and Zhu, Jun Yan and Zhou, Tinghui and Efros, Alexei A.},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Isola et al. - 2017 - Image-to-image translation with conditional adversarial networks.pdf:pdf},
journal = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title = {{Image-to-image translation with conditional adversarial networks}},
year = {2017}
}
@article{Kata2018a,
author = {Kata, Kub{\'{i}}nyi and Kata, R{\'{a}}cz},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kata, Kata - 2018 - skandinavisztikai foly{\'{o}}irat 1(2).pdf:pdf},
title = {skandinavisztikai foly{\'{o}}irat 1.},
year = {2018}
}
@article{Shaham2019,
abstract = {We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. In contrast to previous single image GAN schemes, our approach is not limited to texture images, and is not conditional (i.e. it generates samples from noise). User studies confirm that the generated samples are commonly confused to be real images. We illustrate the utility of SinGAN in a wide range of image manipulation tasks.},
archivePrefix = {arXiv},
arxivId = {1905.01164},
author = {Shaham, Tamar Rott and Dekel, Tali and Michaeli, Tomer},
eprint = {1905.01164},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Shaham, Dekel, Michaeli - 2019 - SinGAN Learning a Generative Model from a Single Natural Image.pdf:pdf},
title = {{SinGAN: Learning a Generative Model from a Single Natural Image}},
url = {http://arxiv.org/abs/1905.01164},
year = {2019}
}
@article{Greenspan2002,
author = {Greenspan, H and Oz, G and Kiryati, N and Peled, S},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Greenspan et al. - 2002 - MRI inter-slice reconstruction using super-resolution.pdf:pdf},
keywords = {echo-planar imaging,epi,fast spin echo,fse,magnetic resonance angiography,mra,signal-to-noise ratio,snr,superresolution,thin},
pages = {437--446},
title = {{MRI inter-slice reconstruction using super-resolution}},
volume = {20},
year = {2002}
}
@article{Lin2011,
abstract = {Quantitative breast density is known as a strong risk factor associated with the development of breast cancer. Measurement of breast density based on three-dimensional breast MRI may provide very useful information. One important step for quantitative analysis of breast density on MRI is the correction of field inhomogeneity to allow an accurate segmentation of the fibroglandular tissue (dense tissue). A new bias field correction method by combining the nonparametric nonuniformity normalization (N3) algorithm and fuzzy-C-means (FCM)-based inhomogeneity correction algorithm is developed in this work.},
author = {Lin, Muqing and Chan, Siwa and Chen, Jeon Hor and Chang, Daniel and Nie, Ke and Chen, Shih Ting and Lin, Cheng Ju and Shih, Tzu Ching and Nalcioglu, Orhan and Su, Min Ying},
doi = {10.1118/1.3519869},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lin et al. - 2011 - A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI.pdf:pdf},
issn = {00942405},
journal = {Medical Physics},
keywords = {bias field,fibroglandular tissue segmentation,field inhomogeneity correction},
number = {1},
pages = {5--14},
title = {{A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI}},
volume = {38},
year = {2011}
}
@article{Marrow2014,
abstract = {Purpose: Childhood overweight is one of the most important public health problems facing America today, and time spent watching television has been proposed as a causal factor. This study examines the effects of TV viewing on the trajectory of BMI over the course of adolescence. Methods: We analyzed data on TV viewing and BMI from the NHLBI Growth and Health Study, a panel study of 2,379 White and Black girls followed for up to 10 annual visits, beginning at age 9 or 10. Latent growth curve models were used to estimate the effects of daily TV viewing on the slope of BMI, one using TV viewing at age 10 to predict the trajectory of BMI from ages 11 to 14, and one using TV viewing at age 14 to predict BMI trajectory from ages 15-19. Models controlled for baseline BMI, physical activity, maturation stage, and socio-economic status. Results: For White girls, higher levels of baseline TV viewing were positively associated with a steeper trajectory of BMI for the four years following a baseline visit (ages 11 to 14, on average). TV viewing was not associated with the trajectory of BMI over the last five of the ten visits. For Black girls, TV viewing was not associated with either trajectory of BMI. Conclusions: White girls who watched more TV at baseline showed a steeper increase in BMI over early adolescence compared to girls who watched less TV. TV viewing may be a factor contributing to overweight among young girls},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Zhuge, Ying and Udupa, Jayaram},
doi = {10.1158/0008-5472.CAN-10-4002.BONE},
eprint = {NIHMS150003},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Marrow, Secreted, Protect - 2014 - Gene targeting reveals the role of Oc90 as the essential organizer of the otoconi.pdf:pdf},
isbn = {6176321972},
issn = {15378276},
journal = {International Society of Differentiation},
keywords = {1,all human cells,cilia are microtubule-based organelles,ciliary signaling,ciliopathies,cilium structure and function,intraflagellar transport,length control,notable,pharmacology,that protrude from nearly},
number = {2},
pages = {1--29},
pmid = {1000000221},
title = {{Intensity Standardization Simplifies Brain MR Image Segmentation}},
volume = {83},
year = {2012}
}
@article{Hese2014,
abstract = {In this study we describe and compare four numerical methods for the generation of random fields with a truncated power-law variogram; the Fourier method, the Randomization method, the Hybrid method as well as the Fourier-Wavelet method. We evaluate these methods with respect to their ability to represent the variogram function over a number of spatial scales as well as the Gaussianity of the generated fields. We furthermore compare these methods with respect to computational costs and investigate structural features.Results show that the Randomization method performs well if only a few number of spatial scales (4-6 orders of magnitude) need to be represented. Due to its simpler implementation it can be preferred over the Fourier-Wavelet method. For a larger interval of spatial scales (9-12 orders of magnitude) however, the Randomization method fails to represent the variogram. Under such circumstances the Hybrid method or the Fourier-Wavelet method should be used.The Matlab code, used for the simulations can be accessed on our institution website at http://www.ufz.de/index.php?en=32179. {\textcopyright} 2014 Elsevier Ltd.},
author = {He{\ss}e, Falk and Prykhodko, Vladyslav and Schl{\"{u}}ter, Steffen and Attinger, Sabine},
doi = {10.1016/j.envsoft.2014.01.013},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/He{\ss}e et al. - 2014 - Generating random fields with a truncated power-law variogram Acomparison of several numerical methods.pdf:pdf},
issn = {13648152},
journal = {Environmental Modelling and Software},
keywords = {Connectivity,Fractal fields,Hierarchical fields,Numerical methods,Random fields,Truncated power law,Variogram function},
pages = {32--48},
publisher = {Elsevier Ltd},
title = {{Generating random fields with a truncated power-law variogram: Acomparison of several numerical methods}},
url = {http://dx.doi.org/10.1016/j.envsoft.2014.01.013},
volume = {55},
year = {2014}
}
@article{Valle-Perez2018,
abstract = {Deep neural networks (DNNs) generalize remarkably well without explicit regularization even in the strongly over-parametrized regime where classical learning theory would instead predict that they would severely overfit. While many proposals for some kind of implicit regularization have been made to rationalise this success, there is no consensus for the fundamental reason why DNNs do not strongly overfit. In this paper, we provide a new explanation. By applying a very general probability-complexity bound recently derived from algorithmic information theory (AIT), we argue that the parameter-function map of many DNNs should be exponentially biased towards simple functions. We then provide clear evidence for this strong simplicity bias in a model DNN for Boolean functions, as well as in much larger fully connected and convolutional networks applied to CIFAR10 and MNIST. As the target functions in many real problems are expected to be highly structured, this intrinsic simplicity bias helps explain why deep networks generalize well on real world problems. This picture also facilitates a novel PAC-Bayes approach where the prior is taken over the DNN input-output function space, rather than the more conventional prior over parameter space. If we assume that the training algorithm samples parameters close to uniformly within the zero-error region then the PAC-Bayes theorem can be used to guarantee good expected generalization for target functions producing high-likelihood training sets. By exploiting recently discovered connections between DNNs and Gaussian processes to estimate the marginal likelihood, we produce relatively tight generalization PAC-Bayes error bounds which correlate well with the true error on realistic datasets such as MNIST and CIFAR10 and for architectures including convolutional and fully connected networks.},
archivePrefix = {arXiv},
arxivId = {1805.08522},
author = {Valle-P{\'{e}}rez, Guillermo and Camargo, Chico Q. and Louis, Ard A.},
eprint = {1805.08522},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Valle-P{\'{e}}rez, Camargo, Louis - 2018 - Deep learning generalizes because the parameter-function map is biased towards simple functions.pdf:pdf},
number = {2015},
pages = {1--35},
title = {{Deep learning generalizes because the parameter-function map is biased towards simple functions}},
url = {http://arxiv.org/abs/1805.08522},
year = {2018}
}
@article{Styner2000,
abstract = {This paper presents a new approach to the correction of intensity inhomogeneities in magnetic resonance imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. The new correction method called parametric bias field correction (PABIC) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity field. We assume that the image is composed of pixels assigned to a small number of categories with a priori known statistics. Further we assume that the image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a nonlinear energy minimization problem using an evolution strategy (ES). The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based on homomorphic filtering. Furthermore, PABIC can correct bias distortions much larger than the image contrast. Input parameters are the intensity statistics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with histogram adjustment, making it well suited for normalizing the intensity histogram of datasets from serial studies. We present simulations and a quantitative validation with phantom and test images. A large number of MR image data acquired with breast, surface, and head coils, both in two dimensions and three dimensions, have been processed and demonstrate the versatility and robustness of this new bias correction scheme.},
author = {Styner, Martin},
doi = {10.1109/42.845174},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Styner - 2000 - Parametric estimate of intensity inhomogeneities applied to MRI.pdf:pdf},
isbn = {0278-0062},
issn = {02780062},
journal = {IEEE Transactions on Medical Imaging},
keywords = {brain tissue segmentation,intensity inhomogeneity,mogeneity,mri,nonlinear optimiza-,rf field inho-},
number = {3},
pages = {153--165},
pmid = {10875700},
title = {{Parametric estimate of intensity inhomogeneities applied to MRI}},
volume = {19},
year = {2000}
}
@article{Woolf1945,
author = {Jelenkor},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Woolf et al. - 1945 - Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat.pdf:pdf},
title = {{Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat 1905}},
year = {1945}
}
@article{Riaz2016,
author = {Riaz, Nadeem and Wolden, Suzanne L and Gelblum, Daphna Y and Eric, J},
doi = {10.1002/cncr.27633.Percutaneous},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Riaz et al. - 2016 - HHS Public Access.pdf:pdf},
number = {24},
pages = {6072--6078},
title = {{A deep learning model integrating FCNNs and CRFs for brain tumor segmentation}},
volume = {118},
year = {2016}
}
@article{Wells1994,
author = {Wells, W M and Grimson, W E L and Kikinis, R and Jolesz, F A},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wells et al. - 1994 - Statistical intensity correction of {MRI} data.pdf:pdf},
journal = {Proc. SPIE},
number = {July},
pages = {13--24},
title = {{Statistical intensity correction of {MRI} data}},
volume = {2359},
year = {1994}
}
@article{Jelenkor2019,
author = {Jelenkor},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Unknown - 2019 - Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat 1904.pdf:pdf},
title = {{Irodalmi {\'{e}}s műv{\'{e}}szeti foly{\'{o}}irat 1904}},
year = {2019}
}
@article{Wu2016,
abstract = {Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference. Also, most NMT systems have difficulty with rare words. These issues have hindered NMT's use in practical deployments and services, where both accuracy and speed are essential. In this work, we present GNMT, Google's Neural Machine Translation system, which attempts to address many of these issues. Our model consists of a deep LSTM network with 8 encoder and 8 decoder layers using attention and residual connections. To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder. To accelerate the final translation speed, we employ low-precision arithmetic during inference computations. To improve handling of rare words, we divide words into a limited set of common sub-word units ("wordpieces") for both input and output. This method provides a good balance between the flexibility of "character"-delimited models and the efficiency of "word"-delimited models, naturally handles translation of rare words, and ultimately improves the overall accuracy of the system. Our beam search technique employs a length-normalization procedure and uses a coverage penalty, which encourages generation of an output sentence that is most likely to cover all the words in the source sentence. On the WMT'14 English-to-French and English-to-German benchmarks, GNMT achieves competitive results to state-of-the-art. Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google's phrase-based production system.},
archivePrefix = {arXiv},
arxivId = {1609.08144},
author = {Wu, Yonghui and Schuster, Mike and Chen, Zhifeng and Le, Quoc V. and Norouzi, Mohammad and Macherey, Wolfgang and Krikun, Maxim and Cao, Yuan and Gao, Qin and Macherey, Klaus and Klingner, Jeff and Shah, Apurva and Johnson, Melvin and Liu, Xiaobing and Kaiser, {\L}ukasz and Gouws, Stephan and Kato, Yoshikiyo and Kudo, Taku and Kazawa, Hideto and Stevens, Keith and Kurian, George and Patil, Nishant and Wang, Wei and Young, Cliff and Smith, Jason and Riesa, Jason and Rudnick, Alex and Vinyals, Oriol and Corrado, Greg and Hughes, Macduff and Dean, Jeffrey},
doi = {10.1038/nrn2258},
eprint = {1609.08144},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wu et al. - 2016 - Google's Neural Machine Translation System Bridging the Gap between Human and Machine Translation.pdf:pdf},
isbn = {1471-003X},
issn = {1471003X},
journal = {Nature Reviews Neuroscience},
number = {4},
pages = {292--303},
pmid = {18319728},
title = {{Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation}},
url = {http://arxiv.org/abs/1609.08144},
volume = {9},
year = {2016}
}
@article{,
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Unknown - Unknown - Visual Question Answering for Medical Images.pdf:pdf},
title = {{Visual Question Answering for Medical Images}}
}
@article{Cheng2019,
abstract = {The deep image prior was recently introduced as a prior for natural images. It represents images as the output of a convolutional network with random inputs. For 'inference', gradient descent is performed to adjust network parameters to make the output match observations. This approach yields good performance on a range of image reconstruction tasks. We show that the deep image prior is asymptotically equivalent to a stationary Gaussian process prior in the limit as the number of channels in each layer of the network goes to infinity, and derive the corresponding kernel. This informs a Bayesian approach to inference. We show that by conducting posterior inference using stochastic gradient Langevin dynamics we avoid the need for early stopping, which is a drawback of the current approach, and improve results for denoising and impainting tasks. We illustrate these intuitions on a number of 1D and 2D signal reconstruction tasks.},
archivePrefix = {arXiv},
arxivId = {arXiv:1904.07457v1},
author = {Cheng, Zezhou and Gadelha, Matheus and Maji, Subhransu and Sheldon, Daniel},
doi = {10.1109/CVPR.2019.00559},
eprint = {arXiv:1904.07457v1},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Cheng et al. - 2019 - A bayesian perspective on the deep image prior.pdf:pdf},
isbn = {9781728132938},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
keywords = {Deep Learning,Low-level Vision,Optimization Methods,Statistical Learning},
pages = {5438--5446},
title = {{A bayesian perspective on the deep image prior}},
volume = {2019-June},
year = {2019}
}
@article{Stanley,
abstract = {With a Muslim population estimated at 20 million, the EU-28 enjoys an annual growth rate of around 15 percent in a halal food market worth US$30 billion. France has the largest Muslim population in Europe (an estimated 4.7 million) and its halal market is valued at around $8 billion. Because of its large Muslim population and the dynamism of its halal market, France is considered the hub of the halal market in Europe. Even though the European halal market should grow by around US$100 million in the coming year, the European market is quite fragmented. France has a Muslim population originating mainly from North Africa; Germany, a Muslim population from Turkey; the United Kingdom, from communities in India, Pakistan and Bangladesh; the Netherlands, from Indonesia, Turkey and Morocco; and Spain and Italy have new migration flows mainly from North Africa. The food habits of these diverse populations are extremely different, and products in demand vary by country. American suppliers of halal products must take regional differences into account when looking at each as potential export market.},
author = {Stanley, Duncan},
doi = {10.1016/0223-5234(88)90223-1},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Stanley - Unknown - Planning your PhD Key learning outcomes.pdf:pdf},
isbn = {2017488895},
issn = {17683254},
journal = {European Journal of Medicinal Chemistry},
number = {4},
pages = {408},
pmid = {22337973},
title = {{General information}},
volume = {23},
year = {1988}
}
@article{Wu2017,
abstract = {It is widely observed that deep learning models with learned parameters generalize well, even with much more model parameters than the number of training samples. We systematically investigate the underlying reasons why deep neural networks often generalize well, and reveal the difference between the minima (with the same training error) that generalize well and those they don't. We show that it is the characteristics the landscape of the loss function that explains the good generalization capability. For the landscape of loss function for deep networks, the volume of basin of attraction of good minima dominates over that of poor minima, which guarantees optimization methods with random initialization to converge to good minima. We theoretically justify our findings through analyzing 2-layer neural networks; and show that the low-complexity solutions have a small norm of Hessian matrix with respect to model parameters. For deeper networks, extensive numerical evidence helps to support our arguments.},
archivePrefix = {arXiv},
arxivId = {1706.10239},
author = {Wu, Lei and Zhu, Zhanxing and E, Weinan},
eprint = {1706.10239},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wu, Zhu, E - 2017 - Towards Understanding Generalization of Deep Learning Perspective of Loss Landscapes.pdf:pdf},
title = {{Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes}},
url = {http://arxiv.org/abs/1706.10239},
year = {2017}
}
@article{Senior,
author = {Senior, Andrew W and Evans, Richard and Jumper, John and Kirkpatrick, James and Sifre, Laurent and Green, Tim and Nelson, Alexander W R and Bridgland, Alex and Penedones, Hugo and Qin, Chongli and Augustin, Z and Petersen, Stig and Simonyan, Karen and Crossan, Steve and Kohli, Pushmeet and Jones, David T and Silver, David and Kavukcuoglu, Koray and Hassabis, Demis},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Senior et al. - Unknown - AlphaFold Improved protein structure prediction using potentials from deep learning.pdf:pdf},
title = {{AlphaFold : Improved protein structure prediction using potentials from deep learning}}
}
@article{Laube2018,
abstract = {In this paper we present a method using deep learning to compute parametrizations for B-spline curve approximation. Existing methods consider the computation of parametric values and a knot vector as separate problems. We propose to train interdependent deep neural networks to predict parametric values and knots. We show that it is possible to include B-spline curve approximation directly into the neural network architecture. The resulting parametrizations yield tight approximations and are able to outperform state-of-the-art methods.},
archivePrefix = {arXiv},
arxivId = {1807.08304},
author = {Laube, Pascal and Franz, Matthias O. and Umlauf, Georg},
doi = {10.1109/3DV.2018.00084},
eprint = {1807.08304},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Laube, Franz, Umlauf - 2018 - Deep learning parametrization for B-spline curve approximation.pdf:pdf},
isbn = {9781538684252},
journal = {Proceedings - 2018 International Conference on 3D Vision, 3DV 2018},
keywords = {Approximation,B-Spline,Deep learning,Neural networks,Parametrization},
pages = {691--699},
title = {{Deep learning parametrization for B-spline curve approximation}},
url = {http://arxiv.org/abs/1807.08304},
year = {2018}
}
@article{Papernot2017,
abstract = {Some machine learning applications involve training data that is sensitive, such as the medical histories of patients in a clinical trial. A model may inadvertently and implicitly store some of its training data; careful analysis of the model may therefore reveal sensitive information. To address this problem, we demonstrate a generally applicable approach to providing strong privacy guarantees for training data: Private Aggregation of Teacher Ensembles (PATE). The approach combines, in a black-box fashion, multiple models trained with disjoint datasets, such as records from different subsets of users. Because they rely directly on sensitive data, these models are not published, but instead used as “teachers” for a “student” model. The student learns to predict an output chosen by noisy voting among all of the teachers, and cannot directly access an individual teacher or the underlying data or parameters. The student's privacy properties can be understood both intuitively (since no single teacher and thus no single dataset dictates the student's training) and formally, in terms of differential privacy. These properties hold even if an adversary can not only query the student but also inspect its internal workings. Compared with previous work, the approach imposes only weak assumptions on how teachers are trained: it applies to any model, including non-convex models like DNNs. We achieve state-of-the-art privacy/utility trade-offs on MNIST and SVHN thanks to an improved privacy analysis and semi-supervised learning.},
archivePrefix = {arXiv},
arxivId = {1610.05755},
author = {Papernot, Nicolas and Goodfellow, Ian and Abadi, Mart{\'{i}}n and Talwar, Kunal and Erlingsson, {\'{U}}lfar},
eprint = {1610.05755},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Papernot et al. - 2017 - Semi-supervised knowledge transfer for deep learning from private training data.pdf:pdf},
journal = {5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings},
number = {2015},
pages = {1--16},
title = {{Semi-supervised knowledge transfer for deep learning from private training data}},
year = {2017}
}
@article{Zbontar2018,
abstract = {Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background.},
archivePrefix = {arXiv},
arxivId = {1811.08839},
author = {Zbontar, Jure and Knoll, Florian and Sriram, Anuroop and Murrell, Tullie and Huang, Zhengnan and Muckley, Matthew J. and Defazio, Aaron and Stern, Ruben and Johnson, Patricia and Bruno, Mary and Parente, Marc and Geras, Krzysztof J. and Katsnelson, Joe and Chandarana, Hersh and Zhang, Zizhao and Drozdzal, Michal and Romero, Adriana and Rabbat, Michael and Vincent, Pascal and Yakubova, Nafissa and Pinkerton, James and Wang, Duo and Owens, Erich and Zitnick, C. Lawrence and Recht, Michael P. and Sodickson, Daniel K. and Lui, Yvonne W.},
eprint = {1811.08839},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Zbontar et al. - 2018 - fastMRI An open dataset and benchmarks for accelerated MRI.pdf:pdf},
journal = {arXiv},
pages = {1--35},
title = {{fastMRI: An open dataset and benchmarks for accelerated MRI}},
year = {2018}
}
@article{Olson2004,
abstract = {The light hydrocarbon sorption properties of the three 8-ring zeolites, pure silica Si-CHA and ITQ-3 and high silica ZSM-58 (DDR structure type), have been measured and the adsorption rates and adsorption energies of ethene, ethane, propane and propene were determined. Of particular interest is the large difference in the rates of adsorption of propene and propane exhibited by each of these zeolites. For Si-CHA the ratio of the D's is 43,000 at 30 °C and the activation energies for diffusion of propene and propane are ∼10 and 73 kJ/mol, respectively, indicating that activated diffusion controls the diffusion rate of propane through its 8-rings. The diffusivity decreases in the order ethene ∼ ethane > propene ≫ propane > n -hexane. All three zeolites have potential for the separation of propene from propene/propane mixtures. {\textcopyright} 2003 Elsevier Inc. All rights reserved.},
archivePrefix = {arXiv},
arxivId = {1102.4807},
author = {Srivastava, Nitish},
doi = {10.1016/j.micromeso.2003.09.025},
eprint = {1102.4807},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ruebel, Stuemke - 2004 - Us 20110178212 a1(2).pdf:pdf},
isbn = {2013023995},
issn = {13871811},
journal = {Microporous and Mesoporous Materials},
keywords = {ITQ-3,Light hydrocarbon sorption,Si-CHA,ZSM-58},
number = {1},
pages = {27--33},
pmid = {23110556},
title = {{Dropout: A Simple Way to Prevent Neural Networks from Overfittin}},
volume = {67},
year = {2004}
}
@article{Jager2007,
abstract = {A major problem of segmentation of magnetic resonance images is that intensities are not standardized like in computed tomography. This article deals with the correction of inter volume intensity differences that lead to a missing anatomical meaning of the observed gray values. We present a method for MRI intensity standardization of whole body MRI scans. The approach is based on the alignment of a learned reference and the current histogram. Each of these histograms is at least 2-d and represents two or more MRI sequences (e.g., T1- and T2-weighted images). From the matching a non-linear correction function is gained which describes a mapping between the intensity spaces and consequently adapts the image statistics to a known standard. As the proposed intensity standardization is based on the statistics of the data sets only, it is independent from spatial coherences or prior segmentations of the reference and newly acquired images. Furthermore, it is not designed for a particular application, body region or acquisition protocol. The method was evaluated on whole body MRI scans containing data sets acquired by T1/FL2D and T2/TIRM sequences. In order to demonstrate the applicability, examples from noisy and pathological image series acquired on a whole body MRI scanner are given.},
author = {J{\"{a}}ger, Florian and Frericks, Bernd and Ny{\'{u}}l, L{\'{a}}szl{\'{o}} and Hornegger, Joachim and Wacker, Frank},
doi = {10.1007/978-3-540-71091-2_92},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/J{\"{a}}ger et al. - 2007 - Whole Body MRI Intensity Standardization.pdf:pdf},
isbn = {3540710906},
issn = {1431472X},
journal = {Bildverarbeitung f{\"{u}}r die Medizin 2007},
pages = {459--463},
title = {{Whole Body MRI Intensity Standardization}},
year = {2007}
}
@article{Zhao2018,
abstract = {High resolution magnetic resonance (MR) imaging (MRI) is desirable in many clinical applications; however, there is a trade-off between resolution, speed of acquisition, and noise. It is common for MR images to have worse through-plane resolution (slice thickness) than in-plane resolution. In these MRI images, high frequency information in the through-plane direction is not acquired, and cannot be resolved through interpolation. To address this issue, super-resolution methods have been developed to enhance spatial resolution. As an ill-posed problem, state-of-the-art super-resolution methods rely on the presence of external/training atlases to learn the transform from low resolution (LR) images to high resolution (HR) images. For several reasons, such HR atlas images are often not available for MRI sequences. This paper presents a self super-resolution (SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image. We use a blurred version of the input image to create training data for a state-of-the-art super-resolution deep network. The trained network is applied to the original input image to estimate the HR image. Our SSR result shows a significant improvement on through-plane resolution compared to competing SSR methods.},
archivePrefix = {arXiv},
arxivId = {1802.09431},
author = {Zhao, Can and Carass, Aaron and Dewey, Blake E. and Prince, Jerry L.},
doi = {10.1109/ISBI.2018.8363594},
eprint = {1802.09431},
file = {:C\:/Users/attil/Downloads/Self_Super-Resolution_for_Magnetic_Resonance_Image.pdf:pdf},
isbn = {9781538636367},
issn = {19458452},
journal = {Proceedings - International Symposium on Biomedical Imaging},
keywords = {CNN,Deep network,MRI,Self super-resolution},
number = {April},
pages = {365--368},
title = {{Self super-resolution for magnetic resonance images using deep networks}},
volume = {2018-April},
year = {2018}
}
@article{Mataev2019,
abstract = {Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. One such contribution, which is the focus of this paper, is the Deep Image Prior (DIP) work by Ulyanov, Vedaldi, and Lempitsky (2018). DIP offers a new approach towards the regularization of inverse problems, obtained by forcing the recovered image to be synthesized from a given deep architecture. While DIP has been shown to be quite an effective unsupervised approach, its results still fall short when compared to state-of-the-art alternatives. In this work, we aim to boost DIP by adding an explicit prior, which enriches the overall regularization effect in order to lead to better-recovered images. More specifically, we propose to bring-in the concept of Regularization by Denoising (RED), which leverages existing denoisers for regularizing inverse problems. Our work shows how the two (DIP and RED) can be merged into a highly effective unsupervised recovery process while avoiding the need to differentiate the chosen denoiser, and leading to very effective results, demonstrated for several tested problems.},
archivePrefix = {arXiv},
arxivId = {1903.10176},
author = {Mataev, Gary and Elad, Michael and Milanfar, Peyman},
eprint = {1903.10176},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mataev, Elad, Milanfar - 2019 - DeepRED Deep image prior powered by RED.pdf:pdf},
journal = {arXiv},
pages = {1--25},
title = {{DeepRED: Deep image prior powered by RED}},
year = {2019}
}
@article{Burger2012,
abstract = {Image denoising can be described as the problem of mapping from a noisy image to a noise-free image. The best currently available denoising methods approximate this mapping with cleverly engineered algorithms. In this work we attempt to learn this mapping directly with a plain multi layer perceptron (MLP) applied to image patches. While this has been done before, we will show that by training on large image databases we are able to compete with the current state-of-the-art image denoising methods. Furthermore, our approach is easily adapted to less extensively studied types of noise (by merely exchanging the training data), for which we achieve excellent results as well. {\textcopyright} 2012 IEEE.},
author = {Burger, Harold C. and Schuler, Christian J. and Harmeling, Stefan},
doi = {10.1109/CVPR.2012.6247952},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Burger, Schuler, Harmeling - 2012 - Image denoising Can plain neural networks compete with BM3D(2).pdf:pdf},
isbn = {9781467312264},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
pages = {2392--2399},
title = {{Image denoising: Can plain neural networks compete with BM3D?}},
year = {2012}
}
@article{Dar2013,
author = {Dar, Salman Ul Hassan and {\"{O}}zbey, Muzaffer and {\c{C}}atlı, Ahmet Burak and {\c{C}}ukur, Tolga},
file = {:C\:/Users/attil/Downloads/1710.02615.pdf:pdf},
number = {Ig 3028},
title = {{A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks}},
year = {2013}
}
@article{Knoll2020,
author = {Knoll, Florian and Hammernik, Kerstin and Kobler, Erich and Pock, Thomas and Michael, P},
doi = {10.1002/mrm.27355.Assessment},
file = {:C\:/Users/attil/Downloads/nihms962219.pdf:pdf},
number = {1},
pages = {116--128},
title = {{Assessment of the generalization of learned image reconstruction and the potential for transfer learning Florian}},
volume = {81},
year = {2020}
}
@article{Welander2018,
abstract = {In medical imaging, a general problem is that it is costly and time consuming to collect high quality data from healthy and diseased subjects. Generative adversarial networks (GANs) is a deep learning method that has been developed for synthesizing data. GANs can thereby be used to generate more realistic training data, to improve classification performance of machine learning algorithms. Another application of GANs is image-to-image translations, e.g. generating magnetic resonance (MR) images from computed tomography (CT) images, which can be used to obtain multimodal datasets from a single modality. Here, we evaluate two unsupervised GAN models (CycleGAN and UNIT) for imageto-image translation of T1- and T2-weighted MR images, by comparing generated synthetic MR images to ground truth images. We also evaluate two supervised models; a modification of CycleGAN and a pure generator model. A small perceptual study was also performed to evaluate how visually realistic the synthesized images are. It is shown that the implemented GAN models can synthesize visually realistic MR images (incorrectly labeled as real by a human). It is also shown that models producing more visually realistic synthetic images not necessarily have better quantitative error measurements, when compared to ground truth data. Code is available at https://github.com/simontomaskarlsson/GAN-MRI.},
author = {Welander, Per and Karlsson, Simon and Eklund, Anders},
file = {:C\:/Users/attil/Downloads/1806.07777.pdf:pdf},
journal = {arXiv},
title = {{Generative adversarial networks for image-to-image translation on multi-contrast mr images - A comparison of cyclegan and unit}},
year = {2018}
}
@article{Wolterink2017,
abstract = {MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis. Current deep learning methods for MR-to-CT synthesis depend on pairwise aligned MR and CT training images of the same patient. However, misalignment between paired images could lead to errors in synthesized CT images. To overcome this, we propose to train a generative adversarial network (GAN) with unpaired MR and CT images. A GAN consisting of two synthesis convolutional neural networks (CNNs) and two discriminator CNNs was trained with cycle consistency to transform 2D brain MR image slices into 2D brain CT image slices and vice versa. Brain MR and CT images of 24 patients were analyzed. A quantitative evaluation showed that the model was able to synthesize CT images that closely approximate reference CT images, and was able to outperform a GAN model trained with paired MR and CT images.},
author = {Wolterink, Jelmer M. and Dinkla, Anna M. and Savenije, Mark H.F. and Seevinck, Peter R. and van den Berg, Cornelis A.T. and I{\v{s}}gum, Ivana},
file = {:C\:/Users/attil/Downloads/1708.01155.pdf:pdf},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {14--23},
title = {{Deep MR to CT synthesis using unpaired data}},
volume = {10557 LNCS},
year = {2017}
}
@article{Fettya,
author = {Fetty, Lukas and L{\"{o}}fsted, L and Furtado, Hugo and Heilemann, Gerd and Nesvacil, Nicole and Nyholm, Tufve and Georg, Dietmar and Kuess, Peter},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Fetty et al. - Unknown - Investigating conditional GAN performance with different generator architectures and an ensemble model for impr.pdf:pdf},
journal = {Phys Med Biol},
keywords = {add,and scanner types,cgan architecture,ga1,if enough,kommenterad,neural networks,radiation therapy,synthetic cts},
title = {{Investigating conditional GAN performance with different generator architectures and an ensemble model for improved MR-sCT conversion}}
}
@article{Ledig2017,
abstract = {Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper con-volutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4× upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method.},
author = {Ledig, Christian and Theis, Lucas and Husz{\'{a}}r, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew P and Tejani, Alykhan and Totz, Johannes and Wang, Zehan and Others},
file = {:C\:/Users/attil/Downloads/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.pdf:pdf},
journal = {Cvpr},
number = {3},
pages = {4},
title = {{Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network}},
url = {http://openaccess.thecvf.com/content_cvpr_2017/papers/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.pdf},
volume = {2},
year = {2017}
}
@article{Mitchell2020,
abstract = {Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor segmen-tation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations. Approach: We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmenta-tions on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap). Results: The neuroradiologists gave technician and DL segmentations mean scores of 6.97 and 7.31, respectively (p < 0.00007). The DL method achieved a mean Dice coefficient of 0.87 on the test cases. Conclusions: This was the first objective comparison of automated and human segmentation using a blinded controlled assessment study. Our DL system learned to outperform its "human teachers" and produced output that was better, on average, than its training data.},
author = {Mitchell, Joseph Ross and Kamnitsas, Konstantinos and Singleton, Kyle W. and Whitmire, Scott A. and Clark-Swanson, Kamala R. and Ranjbar, Sara and Rickertsen, Cassandra R. and Johnston, Sandra K. and Egan, Kathleen M. and Rollison, Dana E. and Arrington, John and Krecke, Karl N. and Passe, Theodore J. and Verdoorn, Jared T. and Nagelschneider, Alex A. and Carr, Carrie M. and Port, John D. and Patton, Alice and Campeau, Norbert G. and Liebo, Greta B. and Eckel, Laurence J. and Wood, Christopher P. and Hunt, Christopher H. and Vibhute, Prasanna and Nelson, Kent D. and Hoxworth, Joseph M. and Patel, Ameet C. and Chong, Brian W. and Ross, Jeffrey S. and Boxerman, Jerrold L. and Vogelbaum, Michael A. and Hu, Leland S. and Glocker, Ben and Swanson, Kristin R.},
doi = {10.1117/1.jmi.7.5.055501},
file = {:C\:/Users/attil/Downloads/055501_1.pdf:pdf},
issn = {2329-4302},
journal = {Journal of Medical Imaging},
number = {05},
title = {{Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data}},
volume = {7},
year = {2020}
}
@article{Anastasiou2020,
abstract = {X-ray detectors are used in medical imaging for the representation of diagnostic information. Digital detector performance is evaluated through appropriate parameters in the spatial domain (i.e. contrast, noise or resolution) and in the frequency domain. Dental radiography is a domain of medical imaging. The purpose of this work is the examination of a commercially available digital dental imaging detector through spatial frequency domain parameters. The available detector was a commercially available image receptor SCHICK CDR, working in indirect mode that is a scintillator coupled to a Complementary Metal Oxide Semiconductor (CMOS) photoreceptor. The detector was irradiated at an X-ray system with 60 kVp and 70 kVp tube voltages utilized in intraoral radiography. The detector linearity, Modulation Transfer Function (MTF), Normalized Noise Power Spectrum (NNPS) and Detective Quantum Efficiency (DQE) were measured according to literature. The Entrance Surface Air Kerma (ESAK) was measured with an RTI PIRANHA X-ray multimeter. The images were evaluated as presented to the dentist by the detector software, in 12bit format. The resolution of the detector was found better than 100 $\mu$m. The DQE curves suggested optimum exposure conditions below 133 $\mu$Gy.},
author = {Anastasiou, A. and Papastamati, F. and Bakas, A. and Michail, C. and Koukou, V. and Martini, N. and Ninos, K. and Lavdas, E. and Valais, I. and Fountos, G. and Kandarakis, I. and Kalyvas, N.},
doi = {10.1016/j.measurement.2019.107171},
file = {:C\:/Users/attil/Downloads/Anastasiou et al. 2020 - Spatial frequency domain analysis of a commercially available digital dental detector.pdf:pdf},
issn = {02632241},
journal = {Measurement: Journal of the International Measurement Confederation},
keywords = {CMOS,DQE,Dental detector,SCHICK CDR},
pages = {107171},
publisher = {Elsevier Ltd},
title = {{Spatial frequency domain analysis of a commercially available digital dental detector}},
url = {https://doi.org/10.1016/j.measurement.2019.107171},
volume = {151},
year = {2020}
}
@article{Mitchell2020a,
abstract = {Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor segmen-tation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations. Approach: We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmenta-tions on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap). Results: The neuroradiologists gave technician and DL segmentations mean scores of 6.97 and 7.31, respectively (p < 0.00007). The DL method achieved a mean Dice coefficient of 0.87 on the test cases. Conclusions: This was the first objective comparison of automated and human segmentation using a blinded controlled assessment study. Our DL system learned to outperform its "human teachers" and produced output that was better, on average, than its training data.},
author = {Mitchell, Joseph Ross and Kamnitsas, Konstantinos and Singleton, Kyle W. and Whitmire, Scott A. and Clark-Swanson, Kamala R. and Ranjbar, Sara and Rickertsen, Cassandra R. and Johnston, Sandra K. and Egan, Kathleen M. and Rollison, Dana E. and Arrington, John and Krecke, Karl N. and Passe, Theodore J. and Verdoorn, Jared T. and Nagelschneider, Alex A. and Carr, Carrie M. and Port, John D. and Patton, Alice and Campeau, Norbert G. and Liebo, Greta B. and Eckel, Laurence J. and Wood, Christopher P. and Hunt, Christopher H. and Vibhute, Prasanna and Nelson, Kent D. and Hoxworth, Joseph M. and Patel, Ameet C. and Chong, Brian W. and Ross, Jeffrey S. and Boxerman, Jerrold L. and Vogelbaum, Michael A. and Hu, Leland S. and Glocker, Ben and Swanson, Kristin R.},
doi = {10.1117/1.jmi.7.5.055501},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mitchell et al. - 2020 - Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the trai.pdf:pdf;:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mitchell et al. - 2020 - Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the t(2).pdf:pdf},
issn = {2329-4302},
journal = {Journal of Medical Imaging},
number = {05},
title = {{Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data}},
volume = {7},
year = {2020}
}
@article{Schwind2021,
author = {Schwind, Janek},
file = {:C\:/Users/attil/Downloads/masterthesis complete.pdf:pdf},
title = {{Ultrafast Information Processing using Photonic Delay Systems: Physical Implementation of the Input Layer, benchmarking and applications}},
year = {2021}
}
@article{Marriott2021,
abstract = {Generative Adversarial Networks (GANs) are able to learn mappings between simple, relatively low-dimensional, random distributions and points on the manifold of realistic images in image-space. The semantics of this mapping, however, are typically entangled such that meaningful image properties cannot be controlled independently of one another. Conditional GANs (cGANs) provide a potential solution to this problem, allowing specific semantics to be enforced during training. This solution, however, depends on the availability of precise labels, which are sometimes difficult or near impossible to obtain, e.g. labels representing lighting conditions or describing the background. In this paper we introduce a new formulation of the cGAN that is able to learn disentangled, multivariate models of semantically meaningful variation and which has the advantage of requiring only the weak supervision of binary attribute labels. For example, given only labels of ambient / non-ambient lighting, our method is able to learn multivariate lighting models disentangled from other factors such as the identity and pose. We coin the method intra-class variation isolation (IVI) and the resulting network the IVI-GAN. We evaluate IVI-GAN on the CelebA dataset and on synthetic 3D morphable model data, learning to disentangle attributes such as lighting, pose, expression, and even the background.},
archivePrefix = {arXiv},
arxivId = {1811.11296},
author = {Marriott, Richard and Romdhani, Sami and Chen, Liming},
doi = {10.1109/fg47880.2020.00042},
eprint = {1811.11296},
file = {:C\:/Users/attil/Downloads/1811.11296.pdf:pdf},
number = {Ivi},
pages = {257--264},
title = {{Taking Control of Intra-class Variation in Conditional GANs Under Weak Supervision}},
year = {2021}
}
@article{Lyu2019,
abstract = {—Magnetic resonance imaging (MRI) is widely used for screening, diagnosis, image-guided therapy, and scientific research. A significant advantage of MRI over other imaging modalities such as computed tomography (CT) and nuclear imaging is that it clearly shows soft tissues in multi-contrasts. Compared with other medical image super-resolution (SR) methods that are in a single contrast, multi-contrast super-resolution studies can synergize multiple contrast images to achieve better super-resolution results. In this paper, we propose a one-level non-progressive neural network for low up-sampling multicontrast super-resolution and a two-level progressive network for high up-sampling multi-contrast super-resolution. Multi-contrast information is combined in high-level feature space. Our experimental results demonstrate that the proposed networks can produce MRI super-resolution images with good image quality and outperform other multi-contrast super-resolution methods in terms of structural similarity and peak signal-to-noise ratio. Also, the progressive network produces a better SR image quality than the non-progressive network, even if the original low-resolution images were highly down-sampled.},
author = {Lyu, Qing and Shan, Hongming and Wang, Ge},
file = {:C\:/Users/attil/Downloads/09001105.pdf:pdf},
issn = {23318422},
journal = {arXiv},
keywords = {Magnetic resonance imaging (MRI),Multi-contrast,Neural network,Progressive neural network,Superresolution (SR)},
number = {9},
pages = {2738--2749},
title = {{Multi-Contrast Super-Resolution MRI Through a Progressive Network}},
volume = {39},
year = {2019}
}
@article{Masutani2020,
abstract = {Background: Cardiac MRI is limited by long acquisition times, yet faster acquisition of smaller-matrix images reduces spatial detail. Deep learning (DL) might enable both faster acquisition and higher spatial detail via super-resolution. Purpose: To explore the feasibility of using DL to enhance spatial detail from small-matrix MRI acquisitions and evaluate its performance against that of conventional image upscaling methods. Materials and Methods: Short-axis cine cardiac MRI examinations performed between January 2012 and December 2018 at one institution were retrospectively collected for algorithm development and testing. Convolutional neural networks (CNNs), a form of DL, were trained to perform super resolution in image space by using synthetically generated low-resolution data. There were 70%, 20%, and 10% of examinations allocated to training, validation, and test sets, respectively. CNNs were compared against bicubic interpolation and Fourier-based zero padding by calculating the structural similarity index (SSIM) between high-resolution ground truth and each upscaling method. Means and standard deviations of the SSIM were reported, and statistical significance was determined by using the Wilcoxon signed-rank test. For evaluation of clinical performance, left ventricular volumes were measured, and statistical significance was determined by using the paired Student t test. Results: For CNN training and retrospective analysis, 400 MRI scans from 367 patients (mean age, 48 years ± 18; 214 men) were included. All CNNs outperformed zero padding and bicubic interpolation at upsampling factors from two to 64 (P <.001). CNNs outperformed zero padding on more than 99.2% of slices (9828 of 9907). In addition, 10 patients (mean age, 51 years ± 22; seven men) were prospectively recruited for super-resolution MRI. Super-resolved low-resolution images yielded left ventricular volumes comparable to those from full-resolution images (P >.05), and super-resolved full-resolution images appeared to further enhance anatomic detail. Conclusion: Deep learning outperformed conventional upscaling methods and recovered high-frequency spatial information. Although training was performed only on short-axis cardiac MRI examinations, the proposed strategy appeared to improve quality in other imaging planes.},
author = {Masutani, Evan M. and Bahrami, Naeim and Hsiao, Albert},
doi = {10.1148/radiol.2020192173},
file = {:C\:/Users/attil/Downloads/radiol.2020192173.pdf:pdf},
issn = {15271315},
journal = {Radiology},
number = {3},
pages = {552--561},
pmid = {32286192},
title = {{Deep learning single-frame and multiframe super-resolution for cardiac MRI}},
volume = {295},
year = {2020}
}
@article{Fisher2018,
abstract = {Restricted Boltzmann Machines (RBMs) are a class of generative neural network that are typically trained to maximize a log-likelihood objective function. We argue that likelihood-based training strategies may fail because the objective does not sufficiently penalize models that place a high probability in regions where the training data distribution has low probability. To overcome this problem, we introduce Boltzmann Encoded Adversarial Machines (BEAMs). A BEAM is an RBM trained against an adversary that uses the hidden layer activations of the RBM to discriminate between the training data and the probability distribution generated by the model. We present experiments demonstrating that BEAMs outperform RBMs and GANs on multiple benchmarks.},
archivePrefix = {arXiv},
arxivId = {1804.08682},
author = {Fisher, Charles K. and Smith, Aaron M. and Walsh, Jonathan R.},
eprint = {1804.08682},
file = {:C\:/Users/attil/Downloads/1804.08682.pdf:pdf},
issn = {23318422},
journal = {arXiv},
title = {{Boltzmann encoded adversarial machines}},
volume = {94108},
year = {2018}
}
@article{Woods1990,
author = {Woods, Peter},
doi = {10.1080/0142569900110108},
file = {:C\:/Users/attil/Downloads/Mao_Least_Squares_Generative_ICCV_2017_paper.pdf:pdf},
issn = {14653346},
journal = {British Journal of Sociology of Education},
number = {1},
pages = {101--117},
title = {{Least Squares Generative Adversarial Networks}},
volume = {11},
year = {1990}
}
@article{Mao2017,
abstract = {Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson X2 divergence. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process. We evaluate LSGANs on LSUN and CIFAR-10 datasets and the experimental results show that the images generated by LSGANs are of better quality than the ones generated by regular GANs. We also conduct two comparison experiments between LSGANs and regular GANs to illustrate the stability of LSGANs.},
archivePrefix = {arXiv},
arxivId = {1611.04076},
author = {Mao, Xudong and Li, Qing and Xie, Haoran and Lau, Raymond Y.K. and Wang, Zhen and Smolley, Stephen Paul},
doi = {10.1109/ICCV.2017.304},
eprint = {1611.04076},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mao et al. - 2017 - Least Squares Generative Adversarial Networks.pdf:pdf},
isbn = {9781538610329},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {2813--2821},
title = {{Least Squares Generative Adversarial Networks}},
volume = {2017-Octob},
year = {2017}
}
@article{Ruppert2009,
abstract = {A standard view of probability and statistics centres on distributions and hypothesis testing. To solve a real problem, say in the spread of disease, one chooses a "model," a distribution or process that is believed from tradition or intuition to be appropriate to the class of problems in question. One uses data to estimate the parameters of the model, and then delivers the resulting exactly specified model to the customer for use in prediction and classification. As a gateway to these mysteries, the combinatorics of dice and coins are recommended; the energetic youth who invest heavily in the calculation of relative frequencies will be inclined to protect their investment through faith in the frequentist philosophy that probabilities are all really relative frequencies. Those with a taste for foundational questions are referred to measure theory, an excursion from which few return.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Ruppert, David},
doi = {10.1198/jasa.2004.s339},
eprint = {arXiv:1011.1669v3},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ruppert - 2009 - The Elements of Statistical Learning Data Mining, Inference, and Prediction.pdf:pdf},
isbn = {9780387848570},
issn = {0162-1459},
journal = {Journal of the American Statistical Association},
number = {466},
pages = {567--567},
pmid = {15512507},
title = {{The Elements of Statistical Learning: Data Mining, Inference, and Prediction}},
volume = {99},
year = {2009}
}
@article{Stanisz2005,
author = {Stanisz, Greg J and Odrobina, Ewa E and Pun, Joseph and Escaravage, Michael and Graham, Simon J and Bronskill, Michael J and Henkelman, R Mark},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Stanisz et al. - 2005 - T 1 , T 2 Relaxation and Magnetization Transfer in Tissue at 3T.pdf:pdf},
journal = {Magnetic Resonance in Medicine},
number = {3},
title = {{T1, T2 relaxation and magnetization transfer in tissue at 3T}},
volume = {54},
year = {2005}
}
@article{Nyholm2014,
author = {Nyholm, Tufve and Jonsson, Joakim},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Nyholm, Jonsson - 2014 - Counterpoint Opportunities and Challenges of a Magnetic Resonance Imaging-Only Radiotherapy Work Flow.pdf:pdf},
journal = {Seminars in Radiation Oncology},
publisher = {Elsevier},
title = {{Counterpoint: Opportunities and Challenges of a Magnetic Resonance Imaging-Only Radiotherapy Work Flow}},
year = {2014}
}
@article{Salimans2016,
author = {Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Salimans et al. - 2016 - Improved techniques for training GANs.pdf:pdf},
journal = {Proceedings of the 30th International Conference on Neural Information Processing Systems},
title = {{Improved techniques for training GANs}},
year = {2016}
}
@article{Fetty2020,
author = {Fetty, Lukas and L{\"{o}}fstedt, Tommy and Heilemann, Gerd and Furtado, Hugo and Nesvacil, Nicole and Nyholm, Tufve and Georg, Dietmar and Kuess, Peter},
file = {:C\:/Users/attil/Downloads/Fetty_2020_Phys._Med._Biol._65_105004.pdf:pdf},
journal = {Physics in Medicine and Biology},
number = {10},
title = {{Investigating conditional GAN performance with different generator architectures, an ensemble model, and different MR scanners for MR-sCT conversion}},
volume = {65},
year = {2020}
}
@article{Margolena1931,
author = {Chen, Yuhua and Shi, Feng and Christodoulou, Anthony G. and Xie, Yibin and Zhou, Zhengwei and Li, Debiao},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Chen et al. - 2018 - Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Conne.pdf:pdf},
journal = {CoRR},
title = {{Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network}},
year = {2018}
}
@misc{Bojorquez2019,
abstract = {The T1 and T2 relaxation times are the basic parameters behind magnetic resonance imaging. The accurate knowledge of the T1 and T2 values of tissues allows to perform quantitative imaging and to develop and optimize magnetic resonance sequences. A vast extent of methods and sequences has been developed to calculate the T1 and T2 relaxation times of different tissues in diverse centers. Surprisingly, a wide range of values has been reported for similar tissues (e.g. T1 of white matter from 699 to 1735 ms and T2 of fat from 41 to 371 ms), and the true values that represent each specific tissue are still unclear, which have deterred their common use in clinical diagnostic imaging. This article presents a comprehensive review of the reported relaxation times in the literature in vivo at 3 T for a large span of tissues. It gives a detailed analysis of the different methods and sequences used to calculate the relaxation times, and it explains the reasons of the spread of reported relaxation times values in the literature.},
author = {Bojorquez, Jorge Zavala and Bricq, St{\'{e}}phanie and Acquitter, Clement and Brunotte, Fran{\c{c}}ois and Walker, Paul M and Lalande, Alain},
booktitle = {Magnetic Resonance Imaging},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Bojorquez et al. - 2019 - What are normal relaxation times of tissues at 3 T.pdf:pdf},
keywords = {3 tesla,Relaxation times,T1,T2},
pages = {69--80},
title = {{What are normal relaxation times of tissues at 3 T?}},
volume = {35},
year = {2017}
}
@article{Gonzalez2008,
author = {Gonzalez, Rafael C. and Woods, Richard E.},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gonzalez, Woods - 2006 - Digital Image Processing (3rd Edition).pdf:pdf},
title = {{Digital Image Processing (3rd Edition)}},
year = {2006}
}
@article{Nilsson,
author = {Nilsson, Erik},
file = {:C\:/Users/attil/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Nilsson - Unknown - Super-Resolution for Fast Multi-Contrast Magnetic Resonance Imaging.pdf:pdf},
title = {{Super-Resolution for Fast Multi-Contrast Magnetic Resonance Imaging}}
}
