@Article{Simarro2020,
  author        = {Jaime Simarro and Ezequiel de la Rosa and Thijs Vande Vyvere and David Robben and Diana M. Sima},
  journal       = {In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science, vol 12658. Springer, Cham (2021)},
  title         = {Unsupervised 3D Brain Anomaly Detection},
  year          = {2020},
  month         = oct,
  abstract      = {Anomaly detection (AD) is the identification of data samples that do not fit a learned data distribution. As such, AD systems can help physicians to determine the presence, severity, and extension of a pathology. Deep generative models, such as Generative Adversarial Networks (GANs), can be exploited to capture anatomical variability. Consequently, any outlier (i.e., sample falling outside of the learned distribution) can be detected as an abnormality in an unsupervised fashion. By using this method, we can not only detect expected or known lesions, but we can even unveil previously unrecognized biomarkers. To the best of our knowledge, this study exemplifies the first AD approach that can efficiently handle volumetric data and detect 3D brain anomalies in one single model. Our proposal is a volumetric and high-detail extension of the 2D f-AnoGAN model obtained by combining a state-of-the-art 3D GAN with refinement training steps. In experiments using non-contrast computed tomography images from traumatic brain injury (TBI) patients, the model detects and localizes TBI abnormalities with an area under the ROC curve of ~75%. Moreover, we test the potential of the method for detecting other anomalies such as low quality images, preprocessing inaccuracies, artifacts, and even the presence of post-operative signs (such as a craniectomy or a brain shunt). The method has potential for rapidly labeling abnormalities in massive imaging datasets, as well as identifying new biomarkers.},
  archiveprefix = {arXiv},
  comment       = {STPM PhysoNet,},
  doi           = {10.1007/978-3-030-72084-1_13},
  eprint        = {2010.04717},
  file          = {:Simarro2020 - Unsupervised 3D Brain Anomaly Detection.pdf:PDF},
  keywords      = {eess.IV, cs.CV},
  primaryclass  = {eess.IV},
}


@InProceedings{BMVC2016_119,
  author     = {Vassileios Balntas, Edgar Riba, Daniel Ponsa and Krystian Mikolajczyk},
  booktitle  = {Proceedings of the British Machine Vision Conference (BMVC)},
  title      = {Learning local feature descriptors with triplets and shallow convolutional neural networks},
  year       = {2016},
  editor     = {Richard C. Wilson, Edwin R. Hancock and William A. P. Smith},
  month      = {September},
  pages      = {119.1-119.11},
  publisher  = {BMVA Press},
  articleno  = {119},
  doi        = {10.5244/C.30.119},
  isbn       = {1-901725-59-6},
  numpages   = {11},
  readstatus = {skimmed},
  url        = {https://dx.doi.org/10.5244/C.30.119},
}


@Article{Yang2019,
  author        = {Jiancheng Yang and Xiaoyang Huang and Yi He and Jingwei Xu and Canqian Yang and Guozheng Xu and Bingbing Ni},
  journal       = {IEEE Journal of Biomedical and Health Informatics (IEEE JBHI), 2021},
  title         = {Reinventing 2D Convolutions for 3D Images},
  year          = {2019},
  month         = nov,
  abstract      = {There have been considerable debates over 2D and 3D representation learning on 3D medical images. 2D approaches could benefit from large-scale 2D pretraining, whereas they are generally weak in capturing large 3D contexts. 3D approaches are natively strong in 3D contexts, however few publicly available 3D medical dataset is large and diverse enough for universal 3D pretraining. Even for hybrid (2D + 3D) approaches, the intrinsic disadvantages within the 2D / 3D parts still exist. In this study, we bridge the gap between 2D and 3D convolutions by reinventing the 2D convolutions. We propose ACS (axial-coronal-sagittal) convolutions to perform natively 3D representation learning, while utilizing the pretrained weights on 2D datasets. In ACS convolutions, 2D convolution kernels are split by channel into three parts, and convoluted separately on the three views (axial, coronal and sagittal) of 3D representations. Theoretically, ANY 2D CNN (ResNet, DenseNet, or DeepLab) is able to be converted into a 3D ACS CNN, with pretrained weight of a same parameter size. Extensive experiments on several medical benchmarks (including classification, segmentation and detection tasks) validate the consistent superiority of the pretrained ACS CNNs, over the 2D / 3D CNN counterparts with / without pretraining. Even without pretraining, the ACS convolution can be used as a plug-and-play replacement of standard 3D convolution, with smaller model size and less computation.},
  archiveprefix = {arXiv},
  doi           = {10.1109/JBHI.2021.3049452},
  eprint        = {1911.10477},
  file          = {:Yang2019 - Reinventing 2D Convolutions for 3D Images.pdf:PDF},
  keywords      = {eess.IV, cs.CV, cs.LG},
  primaryclass  = {eess.IV},
  ranking       = {rank5},
  readstatus    = {read},
}


@Article{Danon2018,
  author        = {Dov Danon and Hadar Averbuch-Elor and Ohad Fried and Daniel Cohen-Or},
  journal       = {Computational Visual Media},
  title         = {Unsupervised Natural Image Patch Learning},
  year          = {2018},
  month         = jun,
  abstract      = {Learning a metric of natural image patches is an important tool for analyzing images. An efficient means is to train a deep network to map an image patch to a vector space, in which the Euclidean distance reflects patch similarity. Previous attempts learned such an embedding in a supervised manner, requiring the availability of many annotated images. In this paper, we present an unsupervised embedding of natural image patches, avoiding the need for annotated images. The key idea is that the similarity of two patches can be learned from the prevalence of their spatial proximity in natural images. Clearly, relying on this simple principle, many spatially nearby pairs are outliers, however, as we show, the outliers do not harm the convergence of the metric learning. We show that our unsupervised embedding approach is more effective than a supervised one or one that uses deep patch representations. Moreover, we show that it naturally leads itself to an efficient self-supervised domain adaptation technique onto a target domain that contains a common foreground object.},
  archiveprefix = {arXiv},
  eprint        = {1807.03130},
  file          = {:Danon2018 - Unsupervised Natural Image Patch Learning.pdf:PDF},
  keywords      = {cs.CV},
  primaryclass  = {cs.CV},
}


@Article{Wang2021,
  author        = {Guodong Wang and Shumin Han and Errui Ding and Di Huang},
  title         = {Student-Teacher Feature Pyramid Matching for Anomaly Detection},
  year          = {2021},
  month         = mar,
  abstract      = {Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful approach to this issue, which is implemented in the student-teacher framework for its advantages but substantially extends it in terms of both accuracy and efficiency. Given a strong model pre-trained on image classification as the teacher, we distill the knowledge into a single student network with the identical architecture to learn the distribution of anomaly-free images and this one-step transfer preserves the crucial clues as much as possible. Moreover, we integrate the multi-scale feature matching strategy into the framework, and this hierarchical feature matching enables the student network to receive a mixture of multi-level knowledge from the feature pyramid under better supervision, thus allowing to detect anomalies of various sizes. The difference between feature pyramids generated by the two networks serves as a scoring function indicating the probability of anomaly occurring. Due to such operations, our approach achieves accurate and fast pixel-level anomaly detection. Very competitive results are delivered on the MVTec anomaly detection dataset, superior to the state of the art ones.},
  archiveprefix = {arXiv},
  eprint        = {2103.04257},
  file          = {:Wang2021 - Student Teacher Feature Pyramid Matching for Anomaly Detection.pdf:PDF},
  keywords      = {cs.CV},
  primaryclass  = {cs.CV},
  booktitle  = {Proceedings of the British Machine Vision Conference (BMVC)},
  year       = {2021},
  publisher  = {BMVA Press},
}



@Article{Bergmann2019,
  author        = {Paul Bergmann and Michael Fauser and David Sattlegger and Carsten Steger},
  title         = {Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings},
  year          = {2019},
  month         = nov,
  abstract      = {We introduce a powerful student-teacher framework for the challenging problem of unsupervised anomaly detection and pixel-precise anomaly segmentation in high-resolution images. Student networks are trained to regress the output of a descriptive teacher network that was pretrained on a large dataset of patches from natural images. This circumvents the need for prior data annotation. Anomalies are detected when the outputs of the student networks differ from that of the teacher network. This happens when they fail to generalize outside the manifold of anomaly-free training data. The intrinsic uncertainty in the student networks is used as an additional scoring function that indicates anomalies. We compare our method to a large number of existing deep learning based methods for unsupervised anomaly detection. Our experiments demonstrate improvements over state-of-the-art methods on a number of real-world datasets, including the recently introduced MVTec Anomaly Detection dataset that was specifically designed to benchmark anomaly segmentation algorithms.},
  archiveprefix = {arXiv},
  doi           = {10.1109/CVPR42600.2020.00424},
  eprint        = {1911.02357},
  file          = {:Bergmann2019 - Uninformed Students_ Student Teacher Anomaly Detection with Discriminative Latent Embeddings.pdf:PDF},
  keywords      = {cs.CV},
  primaryclass  = {cs.CV},
}


@Article{pmid30831356,
  author  = {Schlegl, T. and ck, P. and Waldstein, S. M. and Langs, G. and Schmidt-Erfurth, U.},
  journal = {Med Image Anal},
  title   = {{f-{A}no{G}{A}{N}: {F}ast unsupervised anomaly detection with generative adversarial networks}},
  year    = {2019},
  month   = {May},
  pages   = {30--44},
  volume  = {54},
}


@Article{pmid32669554,
  author  = {Manera, A. L. and Dadar, M. and Fonov, V. and Collins, D. L.},
  journal = {Sci Data},
  title   = {{{C}erebr{A}, registration and manual label correction of {M}indboggle-101 atlas for {M}{N}{I}-{I}{C}{B}{M}152 template}},
  year    = {2020},
  month   = {Jul},
  number  = {1},
  pages   = {237},
  volume  = {7},
}


@Article{Nyul2000,
  author    = {L.G. Nyul and J.K. Udupa and Xuan Zhang},
  journal   = {IEEE Transactions on Medical Imaging},
  title     = {New variants of a method of MRI scale standardization},
  year      = {2000},
  issn      = {1558-254X},
  pages     = {143--150},
  volume    = {19},
  abstract  = {One of the major drawbacks of magnetic resonance imaging (MRI) has been the lack of a standard and quantifiable interpretation of image intensities. Unlike in other modalities, such as X-ray computerized tomography, MR images taken for the same patient on the same scanner at different times may appear different from each other due to a variety of scanner-dependent variations and, therefore, the absolute intensity values do not have a fixed meaning. The authors have devised a two-step method wherein all images (independent of patients and the specific brand of the MR scanner used) can be transformed in such a may that for the same protocol and body region, in the transformed images similar intensities will have similar tissue meaning. Standardized images can be displayed with fixed windows without the need of per-case adjustment. More importantly, extraction of quantitative information about healthy organs or about abnormalities can be considerably simplified. This paper introduces and compares new variants of this standardizing method that can help to overcome some of the problems with the original method.},
  comment   = {Histogram Matching from torchio},
  date      = {Feb. 2000},
  doi       = {10.1109/42.836373},
  file      = {:Nyul2000 - New Variants of a Method of MRI Scale Standardization.html:URL},
  issue     = {2},
  keywords  = {Magnetic resonance imaging, Standardization, Protocols, Body regions, Humans, Image segmentation, X-ray imaging, Computed tomography, Data mining, Displays},
  publisher = {IEEE},
}


@Article{Siddiquee2019,
  author        = {Md Mahfuzur Rahman Siddiquee and Zongwei Zhou and Nima Tajbakhsh and Ruibin Feng and Michael B. Gotway and Yoshua Bengio and Jianming Liang},
  title         = {Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization},
  year          = {2019},
  month         = aug,
  abstract      = {Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs raises an interesting question: can we train a GAN to remove an object, if present, from an image while otherwise preserving the image? Specifically, can a GAN "virtually heal" anyone by turning his medical image, with an unknown health status (diseased or healthy), into a healthy one, so that diseased regions could be revealed by subtracting those two images? Such a task requires a GAN to identify a minimal subset of target pixels for domain translation, an ability that we call fixed-point translation, which no GAN is equipped with yet. Therefore, we propose a new GAN, called Fixed-Point GAN, trained by (1) supervising same-domain translation through a conditional identity loss, and (2) regularizing cross-domain translation through revised adversarial, domain classification, and cycle consistency loss. Based on fixed-point translation, we further derive a novel framework for disease detection and localization using only image-level annotation. Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization. Implementation is available at https://github.com/jlianglab/Fixed-Point-GAN.},
  archiveprefix = {arXiv},
  eprint        = {1908.06965},
  file          = {:Siddiquee2019 - Learning Fixed Points in Generative Adversarial Networks_ from Image to Image Translation to Disease Detection and Localization.pdf:PDF},
  keywords      = {eess.IV, cs.CV},
  primaryclass  = {eess.IV},
  ranking       = {rank5},
  readstatus    = {read},
}


@Article{Baid2021,
  author        = {Ujjwal Baid and Satyam Ghodasara},
  title         = {The {RSNA}-{ASNR}-{MICCAI} {BraTS} 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification},
  year          = {2021},
  month         = jul,
  abstract      = {The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society. Since its inception, BraTS has been focusing on being a common benchmarking venue for brain glioma segmentation algorithms, with well-curated multi-institutional multi-parametric magnetic resonance imaging (mpMRI) data. Gliomas are the most common primary malignancies of the central nervous system, with varying degrees of aggressiveness and prognosis. The RSNA-ASNR-MICCAI BraTS 2021 challenge targets the evaluation of computational algorithms assessing the same tumor compartmentalization, as well as the underlying tumor's molecular characterization, in pre-operative baseline mpMRI data from 2,040 patients. Specifically, the two tasks that BraTS 2021 focuses on are: a) the segmentation of the histologically distinct brain tumor sub-regions, and b) the classification of the tumor's O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. The performance evaluation of all participating algorithms in BraTS 2021 will be conducted through the Sage Bionetworks Synapse platform (Task 1) and Kaggle (Task 2), concluding in distributing to the top ranked participants monetary awards of $60,000$ collectively.},
  archiveprefix = {arXiv},
  eprint        = {2107.02314},
  file          = {:Baid2021 - The RSNA ASNR MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.pdf:PDF},
  keywords      = {cs.CV},
  primaryclass  = {cs.CV},
}


@Article{Menze2015,
  author          = {Bjoern H. Menze and Andras Jakab and Stefan Bauer and Jayashree Kalpathy-Cramer and Key-van Farahani and Justin Kirby and Yuliya Burren and Nicole Porz and Johannes Slotboom and Roland Wiest},
  journal         = {IEEE transactions on medical imaging},
  title           = {The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).},
  year            = {2015},
  issn            = {1558-254X},
  month           = oct,
  pages           = {1993--2024},
  volume          = {34},
  abstract        = {In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark ({BRATS}) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.},
  citation-subset = {IM},
  completed       = {2016-07-06},
  country         = {United States},
  doi             = {10.1109/TMI.2014.2377694},
  issn-linking    = {0278-0062},
  issue           = {10},
  keywords        = {Algorithms; Benchmarking; Glioma, pathology; Humans; Magnetic Resonance Imaging, methods, standards; Neuroimaging, methods, standards},
  mid             = {NIHMS775317},
  nlm-id          = {8310780},
  owner           = {NLM},
  pmc             = {PMC4833122},
  pmid            = {25494501},
  pubmodel        = {Print-Electronic},
  pubstate        = {ppublish},
  revised         = {2022-03-30},
}


@Article{Bakas2017,
  author          = {Bakas, Spyridon and Akbari, Hamed and Sotiras, Aristeidis and Bilello, Michel and Rozycki, Martin and Kirby, Justin S. and Freymann, John B. and Farahani, Keyvan and Davatzikos, Christos},
  journal         = {Scientific data},
  title           = {Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features.},
  year            = {2017},
  issn            = {2052-4463},
  month           = sep,
  pages           = {170117},
  volume          = {4},
  abstract        = {Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method.},
  chemicals       = {DNA, Neoplasm},
  citation-subset = {IM},
  comment         = {BRaTS Cite},
  completed       = {2018-03-02},
  country         = {England},
  doi             = {10.1038/sdata.2017.117},
  issn-linking    = {2052-4463},
  keywords        = {Brain Neoplasms, diagnostic imaging, genetics; DNA, Neoplasm; Glioma, diagnostic imaging, genetics; Humans; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Multimodal Imaging},
  nlm-id          = {101640192},
  owner           = {NLM},
  pii             = {170117},
  pmc             = {PMC5685212},
  pmid            = {28872634},
  pubmodel        = {Electronic},
  pubstate        = {epublish},
  revised         = {2022-03-30},
}


@Article{Behrendt2022,
  author        = {Finn Behrendt and Marcel Bengs and Frederik Rogge and Julia Krüger and Roland Opfer and Alexander Schlaefer},
  title         = {Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data},
  year          = {2022},
  month         = apr,
  abstract      = {The detection of lesions in magnetic resonance imaging (MRI)-scans of human brains remains challenging, time-consuming and error-prone. Recently, unsupervised anomaly detection (UAD) methods have shown promising results for this task. These methods rely on training data sets that solely contain healthy samples. Compared to supervised approaches, this significantly reduces the need for an extensive amount of labeled training data. However, data labelling remains error-prone. We study how unhealthy samples within the training data affect anomaly detection performance for brain MRI-scans. For our evaluations, we consider three publicly available data sets and use autoencoders (AE) as a well-established baseline method for UAD. We systematically evaluate the effect of impured training data by injecting different quantities of unhealthy samples to our training set of healthy samples from T1-weighted MRI-scans. We evaluate a method to identify falsely labeled samples directly during training based on the reconstruction error of the AE. Our results show that training with impured data decreases the UAD performance notably even with few falsely labeled samples. By performing outlier removal directly during training based on the reconstruction-loss, we demonstrate that falsely labeled data can be detected and removed to mitigate the effect of falsely labeled data. Overall, we highlight the importance of clean data sets for UAD in brain MRI and demonstrate an approach for detecting falsely labeled data directly during training.},
  archiveprefix = {arXiv},
  eprint        = {2204.05778},
  file          = {:Behrendt2022 - Unsupervised Anomaly Detection in 3D Brain MRI Using Deep Learning with Impured Training Data.pdf:PDF},
  keywords      = {eess.IV, cs.CV},
  primaryclass  = {eess.IV},
}


@Article{Schlegl2017,
  author        = {Schlegl, Thomas and Seeböck, Philipp and Waldstein, Sebastian M. and Schmidt-Erfurth, Ursula and Langs, Georg},
  title         = {Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery},
  year          = {2017},
  month         = mar,
  abstract      = {Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High annotation effort and the limitation to a vocabulary of known markers limit the power of such approaches. Here, we perform unsupervised learning to identify anomalies in imaging data as candidates for markers. We propose AnoGAN, a deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, accompanying a novel anomaly scoring scheme based on the mapping from image space to a latent space. Applied to new data, the model labels anomalies, and scores image patches indicating their fit into the learned distribution. Results on optical coherence tomography images of the retina demonstrate that the approach correctly identifies anomalous images, such as images containing retinal fluid or hyperreflective foci.},
  archiveprefix = {arXiv},
  copyright     = {arXiv.org perpetual, non-exclusive license},
  doi           = {10.48550/ARXIV.1703.05921},
  eprint        = {1703.05921},
  file          = {:Schlegl2017 - Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery.pdf:PDF},
  keywords      = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences},
  primaryclass  = {cs.CV},
  publisher     = {arXiv},
}


@Article{Pinaya2021,
  author        = {Pinaya, Walter Hugo Lopez and Tudosiu, Petru-Daniel and Gray, Robert and Rees, Geraint and Nachev, Parashkev and Ourselin, Sebastien and Cardoso, M. Jorge},
  title         = {Unsupervised Brain Anomaly Detection and Segmentation with Transformers},
  year          = {2021},
  month         = feb,
  abstract      = {Pathological brain appearances may be so heterogeneous as to be intelligible only as anomalies, defined by their deviation from normality rather than any specific pathological characteristic. Amongst the hardest tasks in medical imaging, detecting such anomalies requires models of the normal brain that combine compactness with the expressivity of the complex, long-range interactions that characterise its structural organisation. These are requirements transformers have arguably greater potential to satisfy than other current candidate architectures, but their application has been inhibited by their demands on data and computational resource. Here we combine the latent representation of vector quantised variational autoencoders with an ensemble of autoregressive transformers to enable unsupervised anomaly detection and segmentation defined by deviation from healthy brain imaging data, achievable at low computational cost, within relative modest data regimes. We compare our method to current state-of-the-art approaches across a series of experiments involving synthetic and real pathological lesions. On real lesions, we train our models on 15,000 radiologically normal participants from UK Biobank, and evaluate performance on four different brain MR datasets with small vessel disease, demyelinating lesions, and tumours. We demonstrate superior anomaly detection performance both image-wise and pixel-wise, achievable without post-processing. These results draw attention to the potential of transformers in this most challenging of imaging tasks.},
  archiveprefix = {arXiv},
  copyright     = {Creative Commons Attribution 4.0 International},
  doi           = {10.48550/ARXIV.2102.11650},
  eprint        = {2102.11650},
  file          = {:Pinaya2021 - Unsupervised Brain Anomaly Detection and Segmentation with Transformers.pdf:PDF},
  keywords      = {Image and Video Processing (eess.IV), Computer Vision and Pattern Recognition (cs.CV), Quantitative Methods (q-bio.QM), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Biological sciences},
  primaryclass  = {eess.IV},
  publisher     = {arXiv},
}


@Article{Han2020,
  author        = {Han, Changhee and Rundo, Leonardo and Murao, Kohei and Noguchi, Tomoyuki and Shimahara, Yuki and Milacski, Zoltan Adam and Koshino, Saori and Sala, Evis and Nakayama, Hideki and Satoh, Shinichi},
  title         = {MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction},
  year          = {2020},
  month         = jul,
  abstract      = {Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence Magnetic Resonance Imaging (MRI) scans. Therefore, we propose unsupervised Medical Anomaly Detection Generative Adversarial Network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 L1 loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average L2 loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1,133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our Self-Attention MADGAN can detect AD on T1 scans at a very early stage, Mild Cognitive Impairment (MCI), with Area Under the Curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921.},
  archiveprefix = {arXiv},
  copyright     = {arXiv.org perpetual, non-exclusive license},
  doi           = {10.48550/ARXIV.2007.13559},
  eprint        = {2007.13559},
  file          = {:http\://arxiv.org/pdf/2007.13559v2:PDF},
  keywords      = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), Image and Video Processing (eess.IV), FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering},
  primaryclass  = {cs.CV},
  publisher     = {arXiv},
}


@Article{Luo2023,
  author          = {Luo, Guoting and Xie, Wei and Gao, Ronghui and Zheng, Tao and Chen, Lei and Sun, Huaiqiang},
  journal         = {Computers in biology and medicine},
  title           = {Unsupervised anomaly detection in brain MRI: Learning abstract distribution from massive healthy brains.},
  year            = {2023},
  issn            = {1879-0534},
  month           = mar,
  pages           = {106610},
  volume          = {154},
  abstract        = {To develop a general unsupervised anomaly detection method based only on MR images of normal brains to automatically detect various brain abnormalities. In this study, a novel method based on three-dimensional deep autoencoder network is proposed to automatically detect and segment various brain abnormalities without being trained on any abnormal samples. A total of 578 normal T2w MR volumes without obvious abnormalities were used for model training and validation. The proposed 3D autoencoder was evaluated on two different datasets (BraTs dataset and in-house dataset) containing T2w volumes from patients with glioblastoma, multiple sclerosis and cerebral infarction. Lesions detection and segmentation performance were reported as AUC, precision-recall curve, sensitivity, and Dice score. In anomaly detection, AUCs for three typical lesions were as follows: glioblastoma, 0.844; multiple sclerosis, 0.858; cerebral infarction, 0.807. In anomaly segmentation, the mean Dice for glioblastomas was 0.462. The proposed network also has the ability to generate an anomaly heatmap for visualization purpose. Our proposed method was able to automatically detect various brain anomalies such as glioblastoma, multiple sclerosis, and cerebral infarction. This work suggests that unsupervised anomaly detection is a powerful approach to detect arbitrary brain abnormalities without labeled samples. It has the potential to support diagnostic workflow in radiology as an automated tool for computer-aided image analysis.},
  citation-subset = {IM},
  completed       = {2023-02-23},
  country         = {United States},
  doi             = {10.1016/j.compbiomed.2023.106610},
  issn-linking    = {0010-4825},
  keywords        = {Humans; Glioblastoma; Brain, diagnostic imaging; Magnetic Resonance Imaging, methods; Cerebral Infarction; Multiple Sclerosis, diagnostic imaging; Image Processing, Computer-Assisted, methods; Anomaly detection; Autoencoder; Brain MRI; Unsupervised learning},
  nlm-id          = {1250250},
  owner           = {NLM},
  pii             = {S0010-4825(23)00075-6},
  pmid            = {36708653},
  pubmodel        = {Print-Electronic},
  pubstate        = {ppublish},
  revised         = {2023-03-20},
}


@InProceedings{An2015VariationalAB,
  author = {Jinwon An and Sungzoon Cho},
  title  = {Variational Autoencoder based Anomaly Detection using Reconstruction Probability},
  year   = {2015},
}


@Misc{ixi,
  author    = {IXI},
  title     = {{IXI}-Dataset},
  groups    = {Biomedical Image Analysis Group},
  timestamp = {2023-05-05},
  url       = {https://brain-development.org/ixi-dataset/},
}


@Article{Akcay2018,
  author        = {Akcay, Samet and Atapour-Abarghouei, Amir and Breckon, Toby P.},
  title         = {GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training},
  year          = {2018},
  month         = may,
  abstract      = {Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While this can be addressed as a supervised learning problem, a significantly more challenging problem is that of detecting the unknown/unseen anomaly case that takes us instead into the space of a one-class, semi-supervised learning paradigm. We introduce such a novel anomaly detection model, by using a conditional generative adversarial network that jointly learns the generation of high-dimensional image space and the inference of latent space. Employing encoder-decoder-encoder sub-networks in the generator network enables the model to map the input image to a lower dimension vector, which is then used to reconstruct the generated output image. The use of the additional encoder network maps this generated image to its latent representation. Minimizing the distance between these images and the latent vectors during training aids in learning the data distribution for the normal samples. As a result, a larger distance metric from this learned data distribution at inference time is indicative of an outlier from that distribution - an anomaly. Experimentation over several benchmark datasets, from varying domains, shows the model efficacy and superiority over previous state-of-the-art approaches.},
  archiveprefix = {arXiv},
  copyright     = {arXiv.org perpetual, non-exclusive license},
  doi           = {10.48550/ARXIV.1805.06725},
  eprint        = {1805.06725},
  file          = {:Akcay2018 - GANomaly_ Semi Supervised Anomaly Detection Via Adversarial Training.pdf:PDF},
  keywords      = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences},
  primaryclass  = {cs.CV},
  publisher     = {arXiv},
}


@Article{Donahue2016,
  author        = {Donahue, Jeff and Krähenbühl, Philipp and Darrell, Trevor},
  title         = {Adversarial Feature Learning},
  year          = {2016},
  month         = may,
  abstract      = {The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution. Intuitively, models trained to predict these semantic latent representations given data may serve as useful feature representations for auxiliary problems where semantics are relevant. However, in their existing form, GANs have no means of learning the inverse mapping -- projecting data back into the latent space. We propose Bidirectional Generative Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and demonstrate that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning.},
  archiveprefix = {arXiv},
  copyright     = {arXiv.org perpetual, non-exclusive license},
  doi           = {10.48550/ARXIV.1605.09782},
  eprint        = {1605.09782},
  file          = {:Donahue2016 - Adversarial Feature Learning.pdf:PDF},
  keywords      = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Computer Vision and Pattern Recognition (cs.CV), Neural and Evolutionary Computing (cs.NE), Machine Learning (stat.ML), FOS: Computer and information sciences},
  primaryclass  = {cs.LG},
  publisher     = {arXiv},
}


@Article{BAUR2021101952,
  author   = {Christoph Baur and Stefan Denner and Benedikt Wiestler and Nassir Navab and Shadi Albarqouni},
  journal  = {Medical Image Analysis},
  title    = {Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study},
  year     = {2021},
  issn     = {1361-8415},
  pages    = {101952},
  volume   = {69},
  abstract = {Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these works is to learn a model of normal anatomy by learning to compress and recover healthy data. This allows to spot abnormal structures from erroneous recoveries of compressed, potentially anomalous samples. The concept is of great interest to the medical image analysis community as it i) relieves from the need of vast amounts of manually segmented training data—a necessity for and pitfall of current supervised Deep Learning—and ii) theoretically allows to detect arbitrary, even rare pathologies which supervised approaches might fail to find. To date, the experimental design of most works hinders a valid comparison, because i) they are evaluated against different datasets and different pathologies, ii) use different image resolutions and iii) different model architectures with varying complexity. The intent of this work is to establish comparability among recent methods by utilizing a single architecture, a single resolution and the same dataset(s). Besides providing a ranking of the methods, we also try to answer questions like i) how many healthy training subjects are needed to model normality and ii) if the reviewed approaches are also sensitive to domain shift. Further, we identify open challenges and provide suggestions for future community efforts and research directions.},
  doi      = {https://doi.org/10.1016/j.media.2020.101952},
  keywords = {Anomaly segmentation, Detection, Unsupervised, Brain MRI, Autoencoder, Variational, Adversarial, Generative, VAE-GAN, VAEGAN},
  url      = {https://www.sciencedirect.com/science/article/pii/S1361841520303169},
}


@Article{Hinton2015,
  author        = {Geoffrey Hinton and Oriol Vinyals and Jeff Dean},
  title         = {Distilling the Knowledge in a Neural Network},
  year          = {2015},
  month         = mar,
  abstract      = {A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel.},
  archiveprefix = {arXiv},
  eprint        = {1503.02531},
  file          = {:Hinton2015 - Distilling the Knowledge in a Neural Network.pdf:PDF},
  keywords      = {stat.ML, cs.LG, cs.NE},
  primaryclass  = {stat.ML},
  priority      = {prio1},
}







