% This file was created with Citavi 6.12.0.177

@proceedings{.2023c,
 abstract = {},
 year = {2023},
 title = {2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)}
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@proceedings{.,
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 year = {2021},
 title = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
 doi = {10.1109/ISBI48211.2021}
}


@proceedings{.1998,
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@proceedings{.2003,
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@proceedings{.2011,
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@proceedings{.2011b,
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 year = {2011},
 title = {(Proceedings of the 28th International Conference on International Conference on Machine Learning},
 address = {Bellevue, Washington, USA},
 publisher = {Omnipress},
 series = {ICML'11}
}


@proceedings{.2012,
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 year = {2012},
 volume = {8314},
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@proceedings{.2013,
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@proceedings{.2013b,
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@proceedings{.2013c,
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@proceedings{.2013d,
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 title = {Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS13)},
 address = {Red Hook, NY, USA},
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 publisher = {{Curran Associates Inc}},
 series = {NIPS'13}
}


@proceedings{.2016,
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 year = {2016},
 volume = {9784},
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@proceedings{.2017,
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 year = {2017},
 title = {2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 address = {Honolulu, HI, USA}
}


@proceedings{.2017b,
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 year = {2017},
 title = {5th International Conference on Learning Representations, ICLR 2017},
 address = {Toulon, France}
}


@proceedings{.2018,
 abstract = {},
 year = {2018},
 volume = {10575},
 publisher = {SPIE}
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@proceedings{.2019,
 abstract = {},
 year = {2019},
 title = {2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
 isbn = {1945-8452}
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@proceedings{.2019b,
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 year = {2019},
 volume = {10949},
 publisher = {SPIE}
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 year = {2019},
 volume = {10950},
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@proceedings{.2019d,
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 year = {2019},
 publisher = {{Springer, Cham}}
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@proceedings{.2019e,
 abstract = {},
 year = {2019},
 title = {International Conference on Medical Imaging with Deep Learning (MIDL)},
 publisher = {PMLR},
 series = {Proceedings of Machine Learning Research}
}


@proceedings{.2019f,
 abstract = {},
 year = {2019},
 title = {2019 International Joint Conference on Neural Networks (IJCNN 2019): Budapest, Hungary, 14-19 July 2019},
 address = {Piscataway, NJ},
 publisher = {IEEE},
 isbn = {978-1-7281-1985-4},
 doi = {10.1109/IJCNN46175.2019}
}


@proceedings{.2020,
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 year = {2020},
 volume = {11313},
 publisher = {SPIE}
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@proceedings{.2020b,
 abstract = {},
 year = {2020},
 title = {2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
 isbn = {978-1-5386-9330-8},
 doi = {10.1109/ISBI45749.2020}
}


@proceedings{.2021,
 abstract = {},
 year = {2021},
 publisher = {{Springer, Cham}}
}


@proceedings{.2021b,
 abstract = {},
 year = {2021},
 publisher = {{Springer, Cham}}
}


@proceedings{.2021c,
 abstract = {},
 year = {2021},
 title = {Proceedings of the IEEE/CVF International Conference on Computer Vision}
}


@proceedings{.2021d,
 abstract = {},
 year = {2021},
 title = {Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}
}


@proceedings{.2022,
 abstract = {},
 year = {2022},
 publisher = {{Springer, Cham}}
}


@proceedings{.2022b,
 abstract = {},
 year = {2022},
 publisher = {{Springer, Cham}}
}


@proceedings{.2022c,
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 year = {2022},
 publisher = {{Springer, Cham}}
}


@proceedings{.2022d,
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 year = {2022},
 publisher = {{Springer, Cham}}
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@proceedings{.2022e,
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 year = {2022},
 publisher = {{Springer, Cham}}
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@proceedings{.2022f,
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 year = {2022},
 publisher = {{Springer, Cham}}
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@proceedings{.2022g,
 abstract = {},
 year = {2022},
 title = {Medical Imaging with Deep Learning}
}


@proceedings{.2022h,
 abstract = {},
 year = {2022},
 title = {The Eleventh International Conference on Learning Representations}
}


@proceedings{.2022i,
 abstract = {},
 year = {2022},
 title = {Medical Imaging with Deep Learning}
}


@proceedings{.2022j,
 abstract = {},
 year = {2022},
 title = {International Conference on Medical Image Computing and Computer-Assisted Intervention}
}


@proceedings{.2023,
 abstract = {},
 year = {2023},
 title = {Medical Imaging with Deep Learning}
}


@proceedings{.2023b,
 abstract = {},
 year = {2023},
 title = {ICML 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH)}
}


@proceedings{.b,
 abstract = {},
 year = {2022},
 title = {2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)},
 doi = {10.1109/ISBI52829.2022}
}


@proceedings{.c,
 abstract = {},
 title = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
 publisher = {Springer}
}


@proceedings{.d,
 abstract = {},
 title = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}
}


@inproceedings{10.1007/978-3-030-72084-1_13,
 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 $\backslash$backslash{\%}{\%}. 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.},
 author = {Simarro Viana, Jaime and de la Rosa, Ezequiel and Vande Vyvere, Thijs and Robben, David and Sima, Diana M. and Investigators, CENTER-TBI Participants and},
 title = {Unsupervised 3D Brain Anomaly Detection},
 pages = {133--142},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-030-72084-1},
 editor = {{Crimi, Alessandro and Bakas, Spyridon}},
 booktitle = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries},
 year = {2021},
 address = {Cham}
}


@article{Akkus.2017,
 abstract = {Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.},
 author = {Akkus, Zeynettin and Galimzianova, Alfiia and Hoogi, Assaf and Rubin, Daniel L. and Erickson, Bradley J.},
 year = {2017},
 title = {Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions},
 url = {https://link.springer.com/article/10.1007%2Fs10278-017-9983-4},
 pages = {449--459},
 volume = {30},
 number = {4},
 issn = {1618-727X},
 journal = {Journal of Digital Imaging},
 doi = {10.1007/s10278-017-9983-4}
}


@article{Akrami.2020,
 abstract = {Automated brain lesion detection from multi-spectral MR images can assist clinicians by improving sensitivity as well as specificity. Supervised machine learning methods have been successful in lesion detection. However, these methods usually rely on a large number of manually delineated images for specific imaging protocols and parameters and often do not generalize well to other imaging parameters and demographics. Most recently, unsupervised models such as autoencoders have become attractive for lesion detection since they do not need access to manually delineated lesions. Despite the success of unsupervised models, using pre-trained models on an unseen dataset is still a challenge. This difficulty is because the new dataset may use different imaging parameters, demographics, and different pre-processing techniques. Additionally, using a clinical dataset that has anomalies and outliers can make unsupervised learning challenging since the outliers can unduly affect the performance of the learned models. These two difficulties make unsupervised lesion detection a particularly challenging task. The method proposed in this work addresses these issues using a two-prong strategy: (1) we use a robust variational autoencoder model that is based on robust statistics, specifically the \textgreek{b}-divergence that can be trained with data that has outliers; (2) we use a transfer-learning method for learning models across datasets with different characteristics. Our results on MRI datasets demonstrate that we can improve the accuracy of lesion detection by adapting robust statistical models and transfer learning for a variational autoencoder model.},
 author = {Akrami, Haleh and Joshi, Anand A. and Li, Jian and Aydore, Sergul and Leahy, Richard M.},
 year = {2020},
 title = {Brain Lesion Detection Using A Robust Variational Autoencoder and Transfer Learning},
 pages = {786--790},
 journal = {IEEE 17th International Symposium on Biomedical Imaging},
 doi = {10.1109/isbi45749.2020.9098405}
}


@article{Akrami.2021,
 abstract = {The Variational AutoEncoder (VAE) has become one of the most popular models for anomaly detection in applications such as lesion detection in medical images. The VAE is a generative graphical model that is used to learn the data distribution from samples and then generate new samples from this distribution. By training on normal samples, the VAE can be used to detect inputs that deviate from this learned distribution. The VAE models the output as a conditionally independent Gaussian characterized by means and variances for each output dimension. VAEs can therefore use reconstruction probability instead of reconstruction error for anomaly detection. Unfortunately, joint optimization of both mean and variance in the VAE leads to the well-known problem of shrinkage or underestimation of variance. We describe an alternative VAE model, Quantile-Regression VAE (QR-VAE), that avoids this variance shrinkage problem by estimating conditional quantiles for the given input image. Using the estimated quantiles, we compute the conditional mean and variance for input images under the Gaussian model. We then compute reconstruction probability using this model as a principled approach to outlier or anomaly detection. We also show how our approach can be used for heterogeneous thresholding of images for detecting lesions in brain images.},
 author = {Akrami, Haleh and Joshi, Anand and Aydore, Sergul and Leahy, Richard},
 year = {2021},
 title = {Quantile Regression for Uncertainty Estimation in VAEs with Applications to Brain Lesion Detection},
 pages = {689--700},
 volume = {12729},
 issn = {1011-2499},
 journal = {Information processing in medical imaging},
 doi = {10.1007/978-3-030-78191-0{\textunderscore }53}
}


@article{Akrami.2022,
 abstract = {},
 author = {Akrami, Haleh and Joshi, Anand and Aydore, Sergul and Leahy, Richard},
 year = {2022},
 title = {Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection},
 pages = {1--23},
 volume = {1},
 number = {IPMI 2021},
 issn = {2766-905X},
 journal = {Machine Learning for Biomedical Imaging},
 doi = {10.59275/j.melba.2022-6751}
}


@article{Alain.2014,
 abstract = {What do auto-encoders learn about the underlying data-generating distribution? Recent work suggests that some auto-encoder variants do a good job of capturing the local manifold structure of data. This paper clarifies some of these previous observations by showing that minimizing a particular form of regularized reconstruction error yields a reconstruction function that locally characterizes the shape of the data-generating density. We show that the auto-encoder captures the score (derivative of the log-density with respect to the input). It contradicts previous interpretations of reconstruction error as an energy function. Unlike previous results, the theorems provided here are completely generic and do not depend on the parameterization of the auto-encoder: they show what the auto-encoder would tend to if given enough capacity and examples. These results are for a contractive training criterion we show to be similar to the denoising auto-encoder training criterion with small corruption noise, but with contraction applied on the whole reconstruction function rather than just encoder. Similarly to score matching, one can consider the proposed training criterion as a convenient alternative to maximum likelihood because it does not involve a partition function. Finally, we show how an approximate Metropolis-Hastings MCMC can be setup to recover samples from the estimated distribution, and this is confirmed in sampling experiments.},
 author = {Alain, Guillaume and Bengio, Yoshua},
 year = {2014},
 title = {What Regularized Auto-Encoders Learn from the Data-Generating Distribution},
 keywords = {auto-encoders;denoising auto-encoders;generative models;manifold learning;Markov chains;score matching;unsupervised representation learning},
 pages = {3563--3593},
 volume = {15},
 number = {1},
 journal = {Journal of  Machine Learning Research}
}


@article{Alaverdyan.2020,
 abstract = {In this study, we propose a novel anomaly detection model targeting subtle brain lesions in multiparametric MRI. To compensate for the lack of annotated data adequately sampling the heterogeneity of such pathologies, we cast this problem as an outlier detection problem and introduce a novel configuration of unsupervised deep siamese networks to learn normal brain representations using a series of non-pathological brain scans. The proposed siamese network, composed of stacked convolutional autoencoders as subnetworks is designed to map patches extracted from healthy control scans only and centered at the same spatial localization to 'close' representations with respect to the chosen metric in a latent space. It is based on a novel loss function combining a similarity term and a regularization term compensating for the lack of dissimilar pairs. These latent representations are then fed into oc-SVM models at voxel-level to produce anomaly score maps. We evaluate the performance of our brain anomaly detection model to detect subtle epilepsy lesions in multiparametric (T1-weighted, FLAIR) MRI exams considered as normal (MRI-negative). Our detection model trained on 75 healthy subjects and validated on 21 epilepsy patients (with 18 MRI-negatives) achieves a maximum sensitivity of 61{\%} on the MRI-negative lesions, identified among the 5 most suspicious detections on average. It is shown to outperform detection models based on the same architecture but with stacked convolutional or Wasserstein autoencoders as unsupervised feature extraction mechanisms.},
 author = {Alaverdyan, Zaruhi and Jung, Julien and Bouet, Romain and Lartizien, Carole},
 year = {2020},
 title = {Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening},
 url = {https://www.sciencedirect.com/science/article/pii/S1361841519301562},
 pages = {101618},
 volume = {60},
 issn = {1361-8415},
 journal = {Medical Image Analysis},
 doi = {10.1016/j.media.2019.101618}
}


@article{Albu.2020,
 abstract = {},
 author = {Albu, Alexandra and Enescu, Alina and Malag{\`o}, Luigi},
 year = {2020},
 title = {Tumor Detection in Brain MRIs by Computing Dissimilarities in the Latent Space of a Variational AutoEncoder},
 pages = {6},
 volume = {1},
 journal = {Proceedings of the Northern Lights Deep Learning Workshop},
 doi = {10.7557/18.5172}
}


@article{Alex.2017,
 abstract = {The work explores the use of denoising autoencoders (DAEs) for brain lesion detection, segmentation, and false-positive reduction. Stacked denoising autoencoders (SDAEs) were pretrained using a large number of unlabeled patient volumes and fine-tuned with patches drawn from a limited number of patients ([Formula: see text], 40, 65). The results show negligible loss in performance even when SDAE was fine-tuned using 20 labeled patients. Low grade glioma (LGG) segmentation was achieved using a transfer learning approach in which a network pretrained with high grade glioma data was fine-tuned using LGG image patches. The networks were also shown to generalize well and provide good segmentation on unseen BraTS 2013 and BraTS 2015 test data. The manuscript also includes the use of a single layer DAE, referred to as novelty detector (ND). ND was trained to accurately reconstruct nonlesion patches. The reconstruction error maps of test data were used to localize lesions. The error maps were shown to assign unique error distributions to various constituents of the glioma, enabling localization. The ND learns the nonlesion brain accurately as it was also shown to provide good segmentation performance on ischemic brain lesions in images from a different database.},
 author = {Alex, Varghese and Vaidhya, Kiran and Thirunavukkarasu, Subramaniam and Kesavadas, Chandrasekharan and Krishnamurthi, Ganapathy},
 year = {2017},
 title = {Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation},
 url = {https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-4/issue-04/041311/Semisupervised-learning-using-denoising-autoencoders-for-brain-lesion-detection-and/10.1117/1.JMI.4.4.041311.full},
 pages = {041311},
 volume = {4},
 number = {4},
 issn = {2329-4302},
 journal = {Journal of medical imaging (Bellingham, Wash.)},
 doi = {10.1117/1.JMI.4.4.041311}
}


@inproceedings{AlexKarargyros.2018,
 abstract = {SPIE Digital Library Proceedings},
 author = {Karargyros, Alex  and  Syeda-Mahmood, Tanveer},
 title = {Saliency U-Net: A regional saliency map-driven hybrid deep learning network for anomaly segmentation},
 url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10575/2293976/Saliency-U-Net--A-regional-saliency-map-driven-hybrid/10.1117/12.2293976.full},
 pages = {413--418},
 publisher = {SPIE},
 year = {2018},
 doi = {10.1117/12.2293976}
}


@inproceedings{AlfiiaGalimzianova.2013,
 abstract = {SPIE Digital Library Proceedings},
 author = {Galimzianova, Alfiia and {\v{S}}piclin , {\v{Z}}iga and Likar, Bo{\v{s}}tjan  and Pernu{\v{s}}, Franjo },
 title = {Automated segmentation of MS lesions in brain MR images using localized trimmed-likelihood estimation},
 url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/8669/1/Automated-segmentation-of-MS-lesions-in-brain-MR-images-using/10.1117/12.2006381.full},
 pages = {937--943},
 publisher = {SPIE},
 year = {2013},
 doi = {10.1117/12.2006381}
}


@article{AndradedeOliveira.2015,
 abstract = {BACKGROUND

Neuroimaging techniques combined with computational neuroanatomy have been playing a role in the investigation of healthy aging and Alzheimer's disease (AD). The definition of normative rules for brain features is a crucial step to establish typical and atypical aging trajectories.

OBJECTIVE

To introduce an unsupervised pattern recognition method; to define multivariate normative rules of neuroanatomical measures; and to propose a brain abnormality index.

METHODS

This study was based on a machine learning approach (one class classification or novelty detection) to neuroanatomical measures (brain regions, volume, and cortical thickness) extracted from the Alzheimer's Disease Neuroimaging Initiative (ADNI)'s database. We applied a \textgreek{n}-One-Class Support Vector Machine (\textgreek{n}-OC-SVM) trained with data from healthy subjects to build an abnormality index, which was compared with subjects diagnosed with mild cognitive impairment and AD.

RESULTS

The method was able to classify AD subjects as outliers with an accuracy of 84.3{\%} at a false alarm rate of 32.5{\%}. The proposed brain abnormality index was found to be significantly associated with group diagnosis, clinical data, biomarkers, and future conversion to AD.

CONCLUSION

These results suggest that one-class classification may be a promising approach to help in the detection of disease conditions. Our findings support a framework considering the continuum of brain abnormalities from healthy aging to AD, which is correlated with cognitive impairment and biomarkers measurements.},
 author = {{Andrade de Oliveira}, Ailton and Carthery-Goulart, Maria Teresa and {Oliveira J{\'u}nior}, Pedro Paulo de Magalh{\~a}es and Carrettiero, Daniel Carneiro and Sato, Jo{\~a}o Ricardo},
 year = {2015},
 title = {Defining multivariate normative rules for healthy aging using neuroimaging and machine learning: an application to Alzheimer's disease},
 url = {https://pubmed.ncbi.nlm.nih.gov/25079801/},
 pages = {201--212},
 volume = {43},
 number = {1},
 issn = {1875-8908},
 journal = {Journal of Alzheimer's disease : JAD},
 doi = {10.3233/JAD-140189}
}


@inproceedings{AnnaritaDAddabbo.2003,
 abstract = {SPIE Digital Library Proceedings},
 author = {D'Addabbo, Annarita and Ancona, Nicola and Blonda, Palma N. and De Blasi, Roberto },
 title = {Detection of multiple sclerosis lesions in MRIs with an SVM classifier},
 url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/5032/1/Detection-of-multiple-sclerosis-lesions-in-MRIs-with-an-SVM/10.1117/12.480407.full},
 pages = {1367--1374},
 publisher = {SPIE},
 year = {2003},
 doi = {10.1117/12.480407}
}


@article{Aswani.2021,
 abstract = {BACKGROUND

The brain tumor is the growth of abnormal cells inside the brain. These cells can be grown into malignant or benign tumors. Segmentation of tumor from MRI images using image processing techniques started decades back. Image processing based brain tumor segmentation can be divided in to three categories conventional image processing methods, Machine Learning methods and Deep Learning methods. Conventional methods lacks the accuracy in segmentation due to complex spatial variation of tumor. Machine Learning methods stand as a good alternative to conventional methods. Methods like SVM, KNN, Fuzzy and a combination of either of these provide good accuracy with reasonable processing speed. The difficulty in processing the various feature extraction methods and maintain accuracy as per the medical standards still exist as a limitation for machine learning methods. In Deep Learning features are extracted automatically in various stages of the network and maintain accuracy as per the medical standards. Huge database requirement and high computational time is still poses a problem for deep learning. To overcome the limitations specified above we propose an unsupervised dual autoencoder with latent space optimization here. The model require only normal MRI images for its training thus reducing the huge tumor database requirement. With a set of normal class data, an autoencoder can reproduce the feature vector into an output layer. This trained autoencoder works well with normal data while it fails to reproduce an anomaly to the output layer. But a classical autoencoder suffer due to poor latent space optimization. The Latent space loss of classical autoencoder is reduced using an auxiliary encoder along with the feature optimization based on singular value decomposition (SVD). The patches used for training are not traditional square patches but we took both horizontal and vertical patches to keep both local and global appearance features on the training set. An Autoencoder is applied separately for learning both horizontal and vertical patches. While training a logistic sigmoid transfer function is used for both encoder and decoder parts. SGD optimizer is used for optimization with an initial learning rate of .001 and the maximum epochs used are 4000. The network is trained in MATLAB 2018a with a processor capacity of 3.7~GHz with NVIDIA GPU and 16~GB of RAM.

RESULTS

The results are obtained using a patch size of 16 $\times$ 64, 64 $\times$ 16 for horizontal and vertical patches respectively. In Glioma images tumor is not grown from a point rather it spreads randomly. Region filling and connectivity operations are performed to get the final tumor segmentation. Overall the method segments Meningioma better than Gliomas. Three evaluation metrics are considered to measure the performance of the proposed system such as Dice Similarity Coefficient, Positive Predictive Value, and Sensitivity.

CONCLUSION

An unsupervised method for the segmentation of brain tumor from MRI images is proposed here. The proposed dual autoencoder with SVD based feature optimization reduce the latent space loss in the classical autoencoder. The proposed method have advantages in computational efficiency, no need of huge database requirement and better accuracy than machine learning methods. The method is compared Machine Learning methods Like SVM, KNN and supervised deep learning methods like CNN and commentable results are obtained.},
 author = {Aswani, K. and Menaka, D.},
 year = {2021},
 title = {A dual autoencoder and singular value decomposition based feature optimization for the segmentation of brain tumor from MRI images},
 url = {https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-021-00614-3},
 pages = {82},
 volume = {21},
 number = {1},
 issn = {1471-2342},
 journal = {BMC Medical Imaging},
 doi = {10.1186/s12880-021-00614-3}
}


@article{Atlason.2019,
 abstract = {White matter hyperintensities (WMHs) of presumed vascular origin are frequently observed in magnetic resonance images (MRIs) of the elderly. Detection and quantification of WMHs is important to help doctors make diagnoses and evaluate prognosis of their elderly patients, and once quantified, these can act as biomarkers in clinical research studies. Manual delineation of WMHs can be both time-consuming and inconsistent, hence, automatic segmentation methods are often preferred. However, fully automatic methods can be challenging to construct due to the variability in lesion load, placement of lesions, and voxel intensities. Several state-of-the-art lesion segmentation methods based on supervised Convolutional Neural Networks (CNNs) have been proposed. These approaches require manually delineated lesions for training the parameters of the network. Here we present a novel approach for WMH segmentation using a CNN trained in an unsupervised manner, by reconstructing multiple MRI sequences as weighted sums of segmentations of WMHs and tissues present in the images. After training, our method can be used to segment new images that are not part of the training set to provide fast and robust segmentation of WMHs in a matter of seconds per subject. Comparisons with state-of-the-art WMH segmentation methods evaluated on ground truth manual labels from two distinct data sets and six different scanners indicate that the proposed method works well at generating accurate WMH segmentations without the need for manual delineations.},
 author = {Atlason, Hans and Love, Askell and Sigurdsson, Sigurdur and Gudnason, Vilmundur and Ellingsen, Lotta M.},
 year = {2019},
 title = {SegAE: Unsupervised white matter lesion segmentation from brain MRIs using a CNN autoencoder},
 pages = {102085},
 volume = {24},
 issn = {2213-1582},
 journal = {NeuroImage: Clinical},
 doi = {10.1016/j.nicl.2019.102085}
}


@article{Atlason.2019b,
 abstract = {SPIE Digital Library Proceedings},
 year = {2019},
 author = {Atlason, Hans and {Askell Love M.D.} and {Sigurdur Sigurdsson} and {Vilmundur Gudnason M.D.} and {Lotta M. Ellingsen}},
 title = {Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder},
 pages = {372--378},
 doi = {10.1117/12.2512953}
}


@article{Baid.2021,
 abstract = {},
 author = {Baid, Ujjwal and Ghodasara, Satyam and Mohan, Suyash and Bilello, Michel and Calabrese, Evan and Colak, Errol and Farahani, Keyvan and Kalpathy-Cramer, Jayashree and Kitamura, Felipe C. and Pati, Sarthak and others},
 year = {2021},
 title = {The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification},
 journal = {arXiv preprint arXiv:2107.02314}
}


@article{Bakas.2017,
 abstract = {},
 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},
 year = {2017},
 title = {Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features},
 pages = {1--13},
 volume = {4},
 number = {1},
 journal = {Scientific data}
}


@article{Bauer.2013,
 abstract = {MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.},
 author = {Bauer, Stefan and Wiest, Roland and Nolte, Lutz-P and Reyes, Mauricio},
 year = {2013},
 title = {A survey of MRI-based medical image analysis for brain tumor studies},
 keywords = {Brain Neoplasms/pathology;Brain/pathology;Humans;Image Enhancement/methods;Magnetic Resonance Imaging/methods},
 pages = {R97-129},
 volume = {58},
 number = {13},
 journal = {Physics in medicine and biology},
 doi = {10.1088/0031-9155/58/13/R97}
}


@article{Baur.2019,
 abstract = {Fusing Unsupervised and Supervised Deep Learning for White Matter Lesion SegmentationChristoph Baur,~Benedikt Wiestler,~Shadi Albarqouni,~Nass...},
 author = {Baur, Christoph and {Benedikt Wiestler} and {Shadi Albarqouni} and {Nassir Navab}},
 year = {2019},
 title = {Fusing Unsupervised and Supervised Deep Learning for White Matter Lesion Segmentation},
 url = {https://proceedings.mlr.press/v102/baur19a.html},
 pages = {63--72},
 issn = {2640-3498},
 journal = {International Conference on Medical Imaging with Deep Learning}
}


@inproceedings{Baur.2019b,
 abstract = {},
 author = {Baur, Christoph and Wiestler, Benedikt and Albarqouni, Shadi and Navab, Nassir},
 title = {Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images},
 pages = {161--169},
 volume = {11383},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-030-11722-1},
 series = {Image Processing, Computer Vision, Pattern Recognition, and Graphics},
 editor = {Crimi, Alessandro and Bakas, Spyridon and Kuijf, Hugo and Keyvan, Farahani and Reyes, Mauricio and {van Walsum}, Theo},
 booktitle = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries},
 year = {2019},
 address = {Cham},
 doi = {10.1007/978-3-030-11723-8{\textunderscore }16}
}


@inproceedings{Baur.2020,
 abstract = {},
 author = {Baur, Christoph and Wiestler, Benedikt and Albarqouni, Shadi and Navab, Nassir},
 title = {Bayesian Skip-Autoencoders for Unsupervised Hyperintense Anomaly Detection in High Resolution Brain Mri},
 pages = {1905--1909},
 isbn = {978-1-5386-9330-8},
 booktitle = {2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
 year = {2020},
 doi = {10.1109/ISBI45749.2020.9098686}
}


@inproceedings{Baur.2020b,
 abstract = {},
 author = {Baur, Christoph and Graf, Robert and Wiestler, Benedikt and Albarqouni, Shadi and Navab, Nassir},
 title = {SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI},
 pages = {718--727},
 volume = {12262},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-030-59712-2},
 series = {Lecture Notes in Computer Science},
 editor = {Martel, Anne L. and Abolmaesumi, Purang and Stoyanov, Danail and Mateus, Diana and Zuluaga, Maria A. and Zhou, S. Kevin and Racoceanu, Daniel and Joskowicz, Leo},
 booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020},
 year = {2020}
}


@inproceedings{Baur.2020c,
 abstract = {},
 author = {Baur, Christoph and Wiestler, Benedikt and Albarqouni, Shadi and Navab, Nassir},
 title = {Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI},
 pages = {552--561},
 volume = {12264},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-030-59712-2},
 series = {Lecture Notes in Computer Science},
 editor = {Martel, Anne L. and Abolmaesumi, Purang and Stoyanov, Danail and Mateus, Diana and Zuluaga, Maria A. and Zhou, S. Kevin and Racoceanu, Daniel and Joskowicz, Leo},
 booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020},
 year = {2020},
 doi = {10.1007/978-3-030-59719-1{\textunderscore }54}
}


@article{Baur.2021,
 abstract = {Purpose

To develop an unsupervised deep learning model on MR images of normal brain anatomy to automatically detect deviations indicative of pathologic states on abnormal MR images.

Materials and Methods

In this retrospective study, spatial autoencoders with skip-connections (which can learn to compress and reconstruct data) were leveraged to learn the normal variability of the brain from MR scans of healthy individuals. A total of 100 normal, in-house MR scans were used for training. Subsequently, as the model was unable to reconstruct anomalies well, this characteristic was exploited for detecting and delineating various diseases by computing the difference between the input data and their reconstruction. The unsupervised model was compared with a supervised U-Net- and threshold-based classifier trained on data from 50 patients with multiple sclerosis (in-house dataset) and 50 patients from The Cancer Imaging Archive. Both the unsupervised and supervised U-Net models were tested on five different datasets containing MR images of microangiopathy, glioblastoma, and multiple sclerosis. Precision-recall statistics and derivations thereof (mean area under the precision-recall curve, Dice score) were used to quantify lesion detection and segmentation performance.

Results

The unsupervised approach outperformed the naive thresholding approach in lesion detection (mean F1 scores ranging from 17{\%} to 62{\%} vs 6.4{\%} to 15{\%} across the five different datasets) and performed similarly to the supervised U-Net (20{\%}-64{\%}) across a variety of pathologic conditions. This outperformance was mostly driven by improved precision compared with the thresholding approach (mean precisions, 15{\%}-59{\%} vs 3.4{\%}-10{\%}). The model was also developed to create an anomaly heatmap display.

Conclusion

The unsupervised deep learning model was able to automatically detect anomalies on brain MR images with high performance. Supplemental material is available for this article.Keywords: Brain/Brain Stem Computer Aided Diagnosis (CAD), Convolutional Neural Network (CNN), Experimental Investigations, Head/Neck, MR-Imaging, Quantification, Segmentation, Stacked Auto-Encoders, Technology Assessment, Tissue Characterization {\copyright} RSNA, 2021.},
 author = {Baur, Christoph and Wiestler, Benedikt and Muehlau, Mark and Zimmer, Claus and Navab, Nassir and Albarqouni, Shadi},
 year = {2021},
 title = {Modeling Healthy Anatomy with Artificial Intelligence for Unsupervised Anomaly Detection in Brain MRI},
 pages = {e190169},
 volume = {3},
 number = {3},
 journal = {Radiology: Artificial Intelligence},
 doi = {10.1148/ryai.2021190169}
}


@article{Baur.2021b,
 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.},
 author = {Baur, Christoph and {Stefan Denner} and {Benedikt Wiestler} and {Nassir Navab} and {Shadi Albarqouni}},
 year = {2021},
 title = {Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study},
 url = {https://www.sciencedirect.com/science/article/pii/S1361841520303169},
 keywords = {Adversarial;Anomaly segmentation;autoencoder;Brain MRI;Detection;Generative;Unsupervised;VAEGAN;VAE-GAN;Variational},
 pages = {101952},
 volume = {69},
 issn = {1361-8415},
 journal = {Medical Image Analysis},
 doi = {10.1016/j.media.2020.101952}
}


@inproceedings{Behrendt.2022,
 abstract = {},
 author = {Behrendt, Finn and Bengs, Marcel and Rogge, Frederik and Kruger, Julia and Opfer, Roland and Schlaefer, Alexander},
 title = {Unsupervised Anomaly Detection in 3D Brain MRI Using Deep Learning with Impured Training Data},
 pages = {1--4},
 booktitle = {2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)},
 year = {2022},
 doi = {10.1109/ISBI52829.2022.9761443}
}


@inproceedings{Behrendt.2022b,
 abstract = {},
 author = {Behrendt, Finn and Bengs, Marcel and Bhattacharya, Debayan and Kr{\"u}ger, Julia and Opfer, Roland and Schlaefer, Alexander},
 title = {Capturing Inter-Slice Dependencies of 3D Brain MRI-Scans for Unsupervised Anomaly Detection},
 url = {https://openreview.net/forum?id=db8wDgKH4p4},
 booktitle = {Medical Imaging with Deep Learning},
 year = {2022}
}


@inproceedings{Behrendt.2023b,
 abstract = {},
 author = {Behrendt, Finn and Bhattacharya, Debayan and Kr{\"u}ger, Julia and Opfer, Roland and Schlaefer, Alexander},
 title = {Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI},
 url = {https://openreview.net/forum?id=O-uZr5S1tJE},
 booktitle = {Medical Imaging with Deep Learning},
 year = {2023}
}



@inproceedings{Bengio.2013,
 abstract = {Recent work has shown how denoising and contractive autoencoders implicitly capture the structure of the data-generating density, in the case where the corruption noise is Gaussian, the reconstruction error is the squared error, and the data is continuous-valued. This has led to various proposals for sampling from this implicitly learned density function, using Langevin and Metropolis-Hastings MCMC. However, it remained unclear how to connect the training procedure of regularized auto-encoders to the implicit estimation of the underlying data-generating distribution when the data are discrete, or using other forms of corruption process and reconstruction errors. Another issue is the mathematical justification which is only valid in the limit of small corruption noise. We propose here a different attack on the problem, which deals with all these issues: arbitrary (but noisy enough) corruption, arbitrary reconstruction loss (seen as a log-likelihood), handling both discrete and continuous-valued variables, and removing the bias due to non-infinitesimal corruption noise (or non-infinitesimal contractive penalty).},
 author = {Bengio, Yoshua and Yao, Li and Alain, Guillaume and Vincent, Pascal},
 title = {Generalized Denoising Auto-Encoders as Generative Models},
 pages = {899--907},
 publisher = {{Curran Associates Inc}},
 series = {NIPS'13},
 booktitle = {Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS13)},
 year = {2013},
 address = {Red Hook, NY, USA}
}


@proceedings{Bengio.2014,
 abstract = {},
 year = {2014},
 title = {2nd International Conference on Learning Representations},
 address = {Banff, AB, Canada},
 series = {ICLR 2014},
 editor = {Bengio, Yoshua and LeCun, Yann}
}


@article{Bengs.2021,
 abstract = {PURPOSE

Brain Magnetic Resonance Images (MRIs) are essential for the diagnosis of neurological diseases. Recently, deep learning methods for unsupervised anomaly detection (UAD) have been proposed for the analysis of brain MRI. These methods rely on healthy brain MRIs and eliminate the requirement of pixel-wise annotated data compared to supervised deep learning. While a wide range of methods for UAD have been proposed, these methods are mostly 2D and only learn from MRI slices, disregarding that brain lesions are inherently 3D and the spatial context of MRI volumes remains unexploited.

METHODS

We investigate whether using increased spatial context by using MRI volumes combined with spatial erasing leads to improved unsupervised anomaly segmentation performance compared to learning from slices. We evaluate and compare 2D variational autoencoder (VAE) to their 3D counterpart, propose 3D input erasing, and systemically study the impact of the data set size on the performance.

RESULTS

Using two publicly available segmentation data sets for evaluation, 3D VAEs outperform their 2D counterpart, highlighting the advantage of volumetric context. Also, our 3D erasing methods allow for further performance improvements. Our best performing 3D VAE with input erasing leads to an average DICE score of 31.40{\%} compared to 25.76{\%} for the 2D VAE.

CONCLUSIONS

We propose 3D deep learning methods for UAD in brain MRI combined with 3D erasing and demonstrate that 3D methods clearly outperform their 2D counterpart for anomaly segmentation. Also, our spatial erasing method allows for further performance improvements and reduces the requirement for large data sets.},
 author = {Bengs, Marcel and Behrendt, Finn and Kr{\"u}ger, Julia and Opfer, Roland and Schlaefer, Alexander},
 year = {2021},
 title = {Three-dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain MRI},
 keywords = {Brain/diagnostic imaging;Deep Learning;Humans;Image Processing, Computer-Assisted;Magnetic Resonance Imaging;Neuroimaging},
 pages = {1413--1423},
 volume = {16},
 number = {9},
 journal = {International journal of computer assisted radiology and surgery},
 doi = {10.1007/s11548-021-02451-9}
}


@inproceedings{Bengs.2022,
 abstract = {},
 author = {Bengs, Marcel and Behrendt, Finn and {Max-Heinrich Laves} and Kr{\"u}ger, Julia and Opfer, Roland and Schlaefer, Alexander},
 title = {Unsupervised anomaly detection in 3D brain MRI using deep learning with multi-task brain age prediction},
 keywords = {3D Autoencoder;anomaly detection;Brain Age Prediction;Brain MRI;Unsupervised},
 pages = {1203314},
 volume = {12033},
 publisher = {SPIE},
 editor = {{Karen Drukker} and {Khan M. Iftekharuddin}},
 booktitle = {Medical Imaging 2022: Computer-Aided Diagnosis},
 year = {2022},
 doi = {10.1117/12.2608120}
}


@article{Bercea.2022,
 abstract = {With the advent of deep learning and increasing use of brain MRIs, a great amount of interest has arisen in automated anomaly segmentation to improve clinical workflows; however, it is time-consuming and expensive to curate medical imaging. Moreover, data are often scattered across many institutions, with privacy regulations hampering its use. Here we present FedDis to collaboratively train an unsupervised deep convolutional autoencoder on 1,532 healthy magnetic resonance scans from four different institutions, and evaluate its performance in identifying pathologies such as multiple sclerosis, vascular lesions, and low- and high-grade tumours/glioblastoma on a total of 538 volumes from six different institutions. To mitigate the statistical heterogeneity among different institutions, we disentangle the parameter space into global (shape) and local (appearance). Four institutes jointly train shape parameters to model healthy brain anatomical structures. Every institute trains appearance parameters locally to allow for client-specific personalization of the global domain-invariant features. We have shown that our collaborative approach, FedDis, improves anomaly segmentation results by 99.74{\%} for multiple sclerosis, 83.33{\%} for vascular lesions and 40.45{\%} for tumours over locally trained models without the need for annotations or sharing of private local data. We found out that FedDis is especially beneficial for institutes that share both healthy and anomaly data, improving their local model performance by up to 227{\%} for multiple sclerosis lesions and 77{\%} for brain tumours.},
 author = {Bercea, Cosmin I. and Wiestler, Benedikt and Rueckert, Daniel and Albarqouni, Shadi},
 year = {2022},
 title = {Federated disentangled representation learning for unsupervised brain anomaly detection},
 pages = {685--695},
 volume = {4},
 number = {8},
 issn = {2522-5839},
 journal = {Nature Machine Intelligence},
 doi = {10.1038/s42256-022-00515-2}
}


@inproceedings{Bercea.2023IMHL,
 abstract = {},
 author = {Bercea, Cosmin and {Michael Neumayr} and {Daniel Rueckert} and {Julia A Schnabel}},
 title = {Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models},
 url = {https://openreview.net/forum?id=kTpafpXrqa},
 booktitle = {ICML 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH)},
 year = {2023}
}


@incollection{Bercea.2023MICa,
 abstract = {},
 author = {Bercea, Cosmin I. and Wiestler, Benedikt and Rueckert, Daniel and Schnabel, Julia A.},
 title = {Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection},
 pages = {293--303},
 volume = {14224},
 year = {2023},
 doi = {10.1007/978-3-031-43904-9_29},
 journal = {Medical Image Computing and Computer Assisted Intervention -- MICCAI23}
}



@article{Bercea.2023MIDL,
 abstract = {Moving beyond hyperintensity thresholding: This paper analyzes the challenges and outlines opportunities for advancing the field of unsupervised anomaly detection.},
 author = {Bercea, Cosmin and {Benedikt Wiestler} and {Daniel Rueckert} and {Julia A Schnabel}},
 year = {2023},
 title = {Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening},
 url = {https://openreview.net/forum?id=8ojx-Ld3yjR},
 issn = {2640-3498},
 journal = {Medical Imaging with Deep Learning}
}


@inproceedings{Bergmann.2020,
 abstract = {},
 author = {Bergmann, Paul and Fauser, Michael and Sattlegger, David and Steger, Carsten},
 title = {Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings},
 pages = {4182--4191},
 booktitle = {2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
 year = {2020}
}


@article{Bergmann.2021,
 abstract = {},
 author = {Bergmann, Paul and Batzner, Kilian and Fauser, Michael and Sattlegger, David and Steger, Carsten},
 year = {2021},
 title = {The MVTec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection},
 pages = {1038--1059},
 volume = {129},
 number = {4},
 journal = {International Journal of Computer Vision}
}


@misc{BiomedicalImageAnalysisGroup.,
 abstract = {},
 author = {{Biomedical Image Analysis Group}},
 title = {IXI Dataset -- Brain Development}
}


@article{Booth.2017,
 abstract = {PURPOSE

To develop an image analysis technique that distinguishes pseudoprogression from true progression by analyzing tumour heterogeneity in T2-weighted images using topological descriptors of image heterogeneity called Minkowski functionals (MFs).

METHODS

Using a retrospective patient cohort (n = 50), and blinded to treatment response outcome, unsupervised feature estimation was performed to investigate MFs for the presence of outliers, potential confounders, and sensitivity to treatment response. The progression and pseudoprogression groups were then unblinded and supervised feature selection was performed using MFs, size and signal intensity features. A support vector machine model was obtained and evaluated using a prospective test cohort.

RESULTS

The model gave a classification accuracy, using a combination of MFs and size features, of more than 85{\%} in both retrospective and prospective datasets. A different feature selection method (Random Forest) and classifier (Lasso) gave the same results. Although not apparent to the reporting radiologist, the T2-weighted hyperintensity phenotype of those patients with progression was heterogeneous, large and frond-like when compared to those with pseudoprogression.

CONCLUSION

Analysis of heterogeneity, in T2-weighted MR images, which are acquired routinely in the clinic, has the potential to detect an earlier treatment response allowing an early change in treatment strategy. Prospective validation of this technique in larger datasets is required.},
 author = {Booth, Thomas C. and Larkin, Timothy J. and Yuan, Yinyin and Kettunen, Mikko I. and Dawson, Sarah N. and Scoffings, Daniel and Canuto, Holly C. and Vowler, Sarah L. and Kirschenlohr, Heide and Hobson, Michael P. and Markowetz, Florian and Jefferies, Sarah and Brindle, Kevin M.},
 year = {2017},
 title = {Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma},
 url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176528},
 pages = {e0176528},
 volume = {12},
 number = {5},
 issn = {1932-6203},
 journal = {PLOS ONE},
 doi = {10.1371/journal.pone.0176528}
}


@article{Bornert.2020,
 abstract = {MRI developed during the last half-century from a very basic concept to an indispensable non-ionising medical imaging technique that has found broad application in diagnostics, therapy control and far beyond. Due to its excellent soft-tissue contrast and the huge variety of accessible tissue- and physiological-parameters, MRI is often preferred to other existing modalities. In the course of its development, MRI underwent many substantial transformations. From the beginning, starting as a proof of concept, much effort was expended to develop the appropriate basic scanning technology and methodology, and to establish the many clinical contrasts (e.g., T1, T2, flow, diffusion, water/fat, etc.) that MRI is famous for today. Beyond that, additional prominent innovations to the field have been parallel imaging and compressed sensing, leading to significant scanning time reductions, and the move towards higher static magnetic field strengths, which led to increased sensitivity and improved image quality. Improvements in workflow and the use of artificial intelligence are among many current trends seen in this field, paving the way for a broad use of MRI. The 125th anniversary of the BJR is a good point to reflect on all these changes and developments and to offer some slightly speculative ideas as to what the future may bring.},
 author = {B{\"o}rnert, Peter and Norris, David G.},
 year = {2020},
 title = {A half-century of innovation in technology-preparing MRI for the 21st century},
 keywords = {Artificial Intelligence/trends;Contrast Media;Deep Learning/trends;Humans;Inventions/trends;Magnetic Resonance Imaging/instrumentation/methods/trends;Magnetics;Workflow},
 pages = {20200113},
 volume = {93},
 number = {1111},
 journal = {The British journal of radiology},
 doi = {10.1259/bjr.20200113}
}


@article{Bowles.2017,
 abstract = {Cerebral small vessel disease (SVD) can manifest in a number of ways. Many of these result in hyperintense regions visible on T2-weighted magnetic resonance (MR) images. The automatic segmentation of these lesions has been the focus of many studies. However, previous methods tended to be limited to certain types of pathology, as a consequence of either restricting the search to the white matter, or by training on an individual pathology. Here we present an unsupervised abnormality detection method which is able to detect abnormally hyperintense regions on FLAIR regardless of the underlying pathology or location. The method uses a combination of image synthesis, Gaussian mixture models and one class support vector machines, and needs only be trained on healthy tissue. We evaluate our method by comparing segmentation results from 127 subjects with SVD with three established methods and report significantly superior performance across a number of metrics.},
 author = {Bowles, Christopher and Qin, Chen and Guerrero, Ricardo and Gunn, Roger and Hammers, Alexander and Dickie, David Alexander and {Vald{\'e}s Hern{\'a}ndez}, Maria and Wardlaw, Joanna and Rueckert, Daniel},
 year = {2017},
 title = {Brain lesion segmentation through image synthesis and outlier detection},
 url = {https://www.sciencedirect.com/science/article/pii/S2213158217302164},
 pages = {643--658},
 volume = {16},
 issn = {2213-1582},
 journal = {NeuroImage: Clinical},
 doi = {10.1016/j.nicl.2017.09.003}
}


@inproceedings{Bredell.2022,
 abstract = {},
 author = {Bredell, Gustav and Flouris, Kyriakos and Chaitanya, Krishna and Erdil, Ertunc and Konukoglu, Ender},
 title = {Explicitly Minimizing the Blur Error of Variational Autoencoders},
 booktitle = {The Eleventh International Conference on Learning Representations},
 year = {2022}
}


@proceedings{Bruijne.2021,
 abstract = {},
 year = {2021},
 title = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2021},
 address = {Cham},
 volume = {12905},
 publisher = {{Springer International Publishing}},
 editor = {de Bruijne, Marleen and Cattin, Philippe C. and Cotin, St{\'e}phane and Padoy, Nicolas and Speidel, Stefanie and Zheng, Yefeng and Essert, Caroline},
 doi = {10.1007/978-3-030-87240-3}
}


@article{Bruno.2015,
 abstract = {Arriving at a medical diagnosis is a highly complex process that is extremely error prone. Missed or delayed diagnoses often lead to patient harm and missed opportunities for treatment. Since medical imaging is a major contributor to the overall diagnostic process, it is also a major potential source of diagnostic error. Although some diagnoses may be missed because of the technical or physical limitations of the imaging modality, including image resolution, intrinsic or extrinsic contrast, and signal-to-noise ratio, most missed radiologic diagnoses are attributable to image interpretation errors by radiologists. Radiologic interpretation cannot be mechanized or automated; it is a human enterprise based on complex psychophysiologic and cognitive processes and is itself subject to a wide variety of error types, including perceptual errors (those in which an important abnormality is simply not seen on the images) and cognitive errors (those in which the abnormality is visually detected but the meaning or importance of the finding is not correctly understood or appreciated). The overall prevalence of radiologists' errors in practice does not appear to have changed since it was first estimated in the 1960s. The authors review the epidemiology of errors in diagnostic radiology, including a recently proposed taxonomy of radiologists' errors, as well as research findings, in an attempt to elucidate possible underlying causes of these errors. The authors also propose strategies for error reduction in radiology. On the basis of current understanding, specific suggestions are offered as to how radiologists can improve their performance in practice.},
 author = {Bruno, Michael A. and Walker, Eric A. and Abujudeh, Hani H.},
 year = {2015},
 title = {Understanding and Confronting Our Mistakes: The Epidemiology of Error in Radiology and Strategies for Error Reduction},
 pages = {1668--1676},
 volume = {35},
 number = {6},
 journal = {Radiographics : a review publication of the Radiological Society of North America, Inc},
 doi = {10.1148/rg.2015150023}
}


@inproceedings{C.Desrosiers.2019,
 abstract = {The segmentation of magnetic resonance data is a challenging task, essential to several clinical and research applications. Since they do not require assistance from a human expert, unsupervised segmentation approaches are especially useful for this task. In this paper, we present two novel unsuper-vised segmentation methods based on random walks. The proposed methods find the probability mode in a local region around pixels, defined by a stochastic diffusion process. As the well-known Hidden Markov Random Field (HMRF) algorithm, these methods can also adapt dynamically to the distribution of pixel intensities by recomputing iteratively the parameters of these distributions. Experiments carried out on real 3D brain MRI from the Internet Brain Segmentation Repository (IBSR) show these methods to be computationally efficient and outperform approaches based on HMRF and Gaussian Mixture Models (GMM), in terms of mean segmentation agreement.},
 author = {Desrosiers, C.},
 title = {An unsupervised random walk approach for the segmentation of brain MRI},
 pages = {337--340},
 isbn = {1945-8452},
 booktitle = {2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
 year = {2019},
 doi = {10.1109/ISBI.2014.6867877}
}


@article{Cai.2023,
 abstract = {Medical anomaly detection is a crucial yet challenging task aimed at recognizing abnormal images to assist in diagnosis. Due to the high-cost annotations of abnormal images, most methods utilize only known normal images during training and identify samples deviating from the normal profile as anomalies in the testing phase. Many readily available unlabeled images containing anomalies are thus ignored in the training phase, restricting the performance. To solve this problem, we introduce one-class semi-supervised learning (OC-SSL) to utilize known normal and unlabeled images for training, and propose Dual-distribution Discrepancy for Anomaly Detection (DDAD) based on this setting. Ensembles of reconstruction networks are designed to model the distribution of normal images and the distribution of both normal and unlabeled images, deriving the normative distribution module (NDM) and unknown distribution module (UDM). Subsequently, the intra-discrepancy of NDM and inter-discrepancy between the two modules are designed as anomaly scores. Furthermore, we propose a new perspective on self-supervised learning, which is designed to refine the anomaly scores rather than directly detect anomalies. Five medical datasets, including chest X-rays, brain MRIs and retinal fundus images, are organized as benchmarks for evaluation. Experiments on these benchmarks comprehensively compare a wide range of anomaly detection methods and demonstrate that our method achieves significant gains and outperforms the state-of-the-art. Code and organized benchmarks are available at https://github.com/caiyu6666/DDAD-ASR.},
 author = {Cai, Yu and Chen, Hao and Yang, Xin and Zhou, Yu and Cheng, Kwang-Ting},
 year = {2023},
 title = {Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images},
 pages = {102794},
 volume = {86},
 issn = {1361-8415},
 journal = {Medical Image Analysis},
 doi = {10.1016/j.media.2023.102794}
}


@book{Cao.2023,
 abstract = {},
 year = {2023},
 title = {Machine Learning in Medical Imaging},
 address = {Cham},
 publisher = {{Springer Nature Switzerland}},
 isbn = {978-3-031-45672-5},
 series = {Lecture Notes in Computer Science},
 editor = {Cao, Xiaohuan and Xu, Xuanang and Rekik, Islem and Cui, Zhiming and Ouyang, Xi},
 doi = {10.1007/978-3-031-45673-2}
}


@article{Carass.2017,
 abstract = {In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.},
 author = {Carass, Aaron and Roy, Snehashis and Jog, Amod and Cuzzocreo, Jennifer L. and Magrath, Elizabeth and Gherman, Adrian and Button, Julia and Nguyen, James and Prados, Ferran and Sudre, Carole H. and {Jorge Cardoso}, Manuel and Cawley, Niamh and Ciccarelli, Olga and Wheeler-Kingshott, Claudia A. M. and Ourselin, S{\'e}bastien and Catanese, Laurence and Deshpande, Hrishikesh and Maurel, Pierre and Commowick, Olivier and Barillot, Christian and Tomas-Fernandez, Xavier and Warfield, Simon K. and Vaidya, Suthirth and Chunduru, Abhijith and Muthuganapathy, Ramanathan and Krishnamurthi, Ganapathy and Jesson, Andrew and Arbel, Tal and Maier, Oskar and Handels, Heinz and Iheme, Leonardo O. and Unay, Devrim and Jain, Saurabh and Sima, Diana M. and Smeets, Dirk and Ghafoorian, Mohsen and Platel, Bram and Birenbaum, Ariel and Greenspan, Hayit and Bazin, Pierre-Louis and Calabresi, Peter A. and Crainiceanu, Ciprian M. and Ellingsen, Lotta M. and Reich, Daniel S. and Prince, Jerry L. and Pham, Dzung L.},
 year = {2017},
 title = {Longitudinal multiple sclerosis lesion segmentation: Resource and challenge},
 keywords = {Adult;Algorithms;Female;Humans;Image Processing, Computer-Assisted;Imaging, Three-Dimensional;Longitudinal Studies;Magnetic Resonance Imaging;Male;Middle Aged;Multiple Sclerosis/diagnostic imaging;Observer Variation;White Matter/diagnostic imaging},
 pages = {77--102},
 volume = {148},
 issn = {1053-8119},
 journal = {NeuroImage},
 doi = {10.1016/j.neuroimage.2016.12.064}
}


@proceedings{Cardoso.2019,
 abstract = {},
 year = {2019},
 title = {Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning},
 publisher = {PMLR},
 series = {Proceedings of Machine Learning Research},
 editor = {Cardoso, M. Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom}
}


@article{Chatterjee.2022,
 abstract = {Expert interpretation of anatomical images of the human brain is the central part of neuroradiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI - even more than one simultaneously, which renders a representation of all possible anomalies very challenging. Hence, a possible solution is an unsupervised anomaly detection (UAD) system that can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out-of-distribution samples. Such a technique can then be used to detect anomalies - lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology. Several Variational Autoencoder (VAE) based techniques have been proposed in the past for this task. Even though they perform very well on controlled artificially simulated anomalies, many of them perform poorly while detecting anomalies in clinical data. This research proposes a compact version of the {\textquotedbl}context-encoding{\textquotedbl} VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA), which is more robust on clinical data and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642~$\pm$~0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859~$\pm$~0.112 while detecting artificially induced anomalies, while the best performing baseline achieved 0.522~$\pm$~0.135 and 0.783~$\pm$~0.111, respectively.},
 author = {Chatterjee, Soumick and Sciarra, Alessandro and D{\"u}nnwald, Max and Tummala, Pavan and Agrawal, Shubham Kumar and Jauhari, Aishwarya and Kalra, Aman and Oeltze-Jafra, Steffen and Speck, Oliver and N{\"u}rnberger, Andreas},
 year = {2022},
 title = {StRegA: Unsupervised anomaly detection in brain MRIs using a compact context-encoding variational autoencoder},
 url = {https://www.sciencedirect.com/science/article/pii/S0010482522008010},
 pages = {106093},
 volume = {149},
 issn = {0010-4825},
 journal = {Computers in Biology and Medicine},
 doi = {10.1016/j.compbiomed.2022.106093}
}


@article{Chen.2018b,
 abstract = {Recent advances in deep learning led to novel generative modeling techniques that achieve unprecedented quality in generated samples and performance in learning complex distributions in imaging data. These new models in medical image computing have important applications that form clinically relevant and very challenging unsupervised learning problems. In this paper, we explore the feasibility of using state-of-the-art auto-encoder-based deep generative models, such as variational and adversarial auto-encoders, for one such task: abnormality detection in medical imaging. We utilize typical, publicly available datasets with brain scans from healthy subjects and patients with stroke lesions and brain tumors. We use the data from healthy subjects to train different auto-encoder based models to learn the distribution of healthy images and detect pathologies as outliers. Models that can better learn the data distribution should be able to detect outliers more accurately. We evaluate the detection performance of deep generative models and compare them with non-deep learning based approaches to provide a benchmark of the current state of research. We conclude that abnormality detection is a challenging task for deep generative models and large room exists for improvement. In order to facilitate further research, we aim to provide carefully pre-processed imaging data available to the research community.},
 author = {Chen, Xiaoran and Pawlowski, Nick and Rajchl, Martin and Glocker, Ben and Konukoglu, Ender},
 year = {2018},
 title = {Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging},
 url = {https://arxiv.org/pdf/1806.05452},
 journal = {arXiv preprint 1806.05452v1}
}


@article{Chen.2020,
 abstract = {Unsupervised lesion detection is a challenging problem that requires accurately estimating normative distributions of healthy anatomy and detecting lesions as outliers without training examples. Recently, this problem has received increased attention from the research community following the advances in unsupervised learning with deep learning. Such advances allow the estimation of high-dimensional distributions, such as normative distributions, with higher accuracy than previous methods. The main approach of the recently proposed methods is to learn a latent-variable model parameterized with networks to approximate the normative distribution using example images showing healthy anatomy, perform prior-projection, i.e. reconstruct the image with lesions using the latent-variable model, and determine lesions based on the differences between the reconstructed and original images. While being promising, the prior-projection step often leads to a large number of false positives. In this work, we approach unsupervised lesion detection as an image restoration problem and propose a probabilistic model that uses a network-based prior as the normative distribution and detect lesions pixel-wise using MAP estimation. The probabilistic model punishes large deviations between restored and original images, reducing false positives in pixel-wise detections. Experiments with gliomas and stroke lesions in brain MRI using publicly available datasets show that the proposed approach outperforms the state-of-the-art unsupervised methods by a substantial margin, +0.13 (AUC), for both glioma and stroke detection. Extensive model analysis confirms the effectiveness of MAP-based image restoration.},
 author = {Chen, Xiaoran and You, Suhang and Tezcan, Kerem Can and Konukoglu, Ender},
 year = {2020},
 title = {Unsupervised lesion detection via image restoration with a normative prior},
 keywords = {Humans;Magnetic Resonance Imaging;Models, Statistical;Stroke},
 pages = {101713},
 volume = {64},
 issn = {1361-8415},
 journal = {Medical Image Analysis},
 doi = {10.1016/j.media.2020.101713}
}


@inproceedings{Chen.2022,
 abstract = {},
 author = {Chen, Xiaoran and Konukoglu, Ender},
 title = {Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders},
 booktitle = {Medical Imaging with Deep Learning},
 year = {2022}
}


@article{ChenxinLi.2021,
 abstract = {},
 author = {Li, Chenxin Li and  Zhang, Yunlong and  Li, Jiongcheng and  Huang, Yue and  Ding,Xinghao},
 year = {2021},
 title = {Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation},
 url = {https://api.semanticscholar.org/CorpusID:232240640},
 volume = {abs/2103.09094},
 journal = {ArXiv}
}


@inproceedings{Cho.2022,
 abstract = {Recent studies on anomaly detection have achieved great success in data analysis, yet the application of out-of-distribution detection in medical imaging remains an underdeveloped area of study. In this paper, we propose a 3D fully self-supervised learning method for...},
 author = {Cho, Jihoon and Kang, Inha and Park, Jinah},
 title = {Self-supervised 3D Out-of-Distribution Detection via Pseudoanomaly Generation},
 url = {https://link.springer.com/chapter/10.1007/978-3-030-97281-3_15},
 pages = {95--103},
 publisher = {{Springer, Cham}},
 year = {2022},
 doi = {10.1007/978-3-030-97281-3{\textunderscore }15}
}


@article{Choi.2019,
 abstract = {BACKGROUND

Recent deep learning models have shown remarkable accuracy for the diagnostic classification. However, they have limitations in clinical application due to the gap between the training cohorts and real-world data. We aimed to develop a model trained only by normal brain PET data with an unsupervised manner to identify an abnormality in various disorders as imaging data of the clinical routine.

METHODS

Using variational autoencoder, a type of unsupervised learning, Abnormality Score was defined as how far a given brain image is from the normal data. The model was applied to FDG PET data of Alzheimer's disease (AD) and mild cognitive impairment (MCI) and clinical routine FDG PET data for assessing behavioral abnormality and seizures. Accuracy was measured by the area under curve (AUC) of receiver-operating-characteristic (ROC) curve. We investigated whether deep learning has additional benefits with experts' visual interpretation to identify abnormal patterns.

FINDINGS

The AUC of the ROC curve for differentiating AD was 0.90. The changes in cognitive scores from baseline to 2-year follow-up were significantly correlated with Abnormality Score at baseline. The AUC of the ROC curve for discriminating patients with various disorders from controls was 0.74. Experts' visual interpretation was helped by the deep learning model to identify abnormal patterns in 60{\%} of cases initially not identified without the model.

INTERPRETATION

We suggest that deep learning model trained only by normal data was applicable for identifying wide-range of abnormalities in brain diseases, even uncommon ones, proposing its possible use for interpreting real-world clinical data.},
 author = {Choi, Hongyoon and Ha, Seunggyun and Kang, Hyejin and Lee, Hyekyoung and Lee, Dong Soo},
 year = {2019},
 title = {Deep learning only by normal brain PET identify unheralded brain anomalies},
 pages = {447--453},
 volume = {43},
 journal = {EBioMedicine},
 doi = {10.1016/j.ebiom.2019.04.022}
}


@proceedings{Cohen.2008,
 abstract = {},
 year = {2008},
 title = {Proceedings of the 25th international conference on Machine learning},
 address = {New York, NY},
 publisher = {ACM},
 isbn = {9781605582054},
 editor = {Cohen, William},
 doi = {10.1145/1390156}
}


@article{Commowick.2018,
 abstract = {We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, {\l}dots), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.},
 author = {Commowick, Olivier and Istace, Audrey and Kain, Micha{\"e}l and Laurent, Baptiste and Leray, Florent and Simon, Mathieu and Pop, Sorina Camarasu and Girard, Pascal and Am{\'e}li, Roxana and Ferr{\'e}, Jean-Christophe and Kerbrat, Anne and Tourdias, Thomas and Cervenansky, Fr{\'e}d{\'e}ric and Glatard, Tristan and Beaumont, J{\'e}r{\'e}my and Doyle, Senan and Forbes, Florence and Knight, Jesse and Khademi, April and Mahbod, Amirreza and Wang, Chunliang and McKinley, Richard and Wagner, Franca and Muschelli, John and Sweeney, Elizabeth and Roura, Eloy and Llad{\'o}, Xavier and Santos, Michel M. and Santos, Wellington P. and Silva-Filho, Abel G. and Tomas-Fernandez, Xavier and Urien, H{\'e}l{\`e}ne and Bloch, Isabelle and Valverde, Sergi and Cabezas, Mariano and Vera-Olmos, Francisco Javier and Malpica, Norberto and Guttmann, Charles and Vukusic, Sandra and Edan, Gilles and Dojat, Michel and Styner, Martin and Warfield, Simon K. and Cotton, Fran{\c{c}}ois and Barillot, Christian},
 year = {2018},
 title = {Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure},
 pages = {13650},
 volume = {8},
 number = {1},
 issn = {2045-2322},
 journal = {Scientific Reports}
}


@proceedings{Crimi.2019,
 abstract = {Brain lesion image analysis.-Brain tumor image segmentation -- Ischemic stroke lesion image segmentation -- Grand challenge on MR brain segmentation -- Computational precision medicine -- Stroke workshop on imaging and treatment challenges



This two-volume set LNCS 11383 and 11384 constitutes revised selected papers from the 4th International MICCAI Brainlesion Workshop, BrainLes 2018, as well as the International Multimodal Brain Tumor Segmentation, BraTS, Ischemic Stroke Lesion Segmentation, ISLES, MR Brain Image Segmentation, MRBrainS18, Computational Precision Medicine, CPM, and Stroke Workshop on Imaging and Treatment Challenges, SWITCH, which were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI, in Granada, Spain, in September 2018. The 92 papers presented in this volume were carefully reviewed and selected from 95 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; ischemic stroke lesion image segmentation; grand challenge on MR brain segmentation; computational precision medicine; stroke workshop on imaging and treatment challenges},
 year = {2019},
 title = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II},
 address = {Cham},
 volume = {11384},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-030-11725-2},
 series = {Lecture Notes in Computer Science},
 editor = {Crimi, Alessandro and Bakas, Spyridon and Kuijf, Hugo and Keyvan, Farahani and Reyes, Mauricio and {van Walsum}, Theo},
 doi = {10.1007/978-3-030-11726-9}
}


@proceedings{Crimi.2019b,
 abstract = {Brain lesion image analysis.-Brain tumor image segmentation -- Ischemic stroke lesion image segmentation -- Grand challenge on MR brain segmentation -- Computational precision medicine -- Stroke workshop on imaging and treatment challenges



This two-volume set LNCS 11383 and 11384 constitutes revised selected papers from the 4th International MICCAI Brainlesion Workshop, BrainLes 2018, as well as the International Multimodal Brain Tumor Segmentation, BraTS, Ischemic Stroke Lesion Segmentation, ISLES, MR Brain Image Segmentation, MRBrainS18, Computational Precision Medicine, CPM, and Stroke Workshop on Imaging and Treatment Challenges, SWITCH, which were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI, in Granada, Spain, in September 2018. The 92 papers presented in this volume were carefully reviewed and selected from 95 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; ischemic stroke lesion image segmentation; grand challenge on MR brain segmentation; computational precision medicine; stroke workshop on imaging and treatment challenges},
 year = {2019},
 title = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I},
 address = {Cham},
 volume = {11383},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-030-11722-1},
 series = {Image Processing, Computer Vision, Pattern Recognition, and Graphics},
 editor = {Crimi, Alessandro and Bakas, Spyridon and Kuijf, Hugo and Keyvan, Farahani and Reyes, Mauricio and {van Walsum}, Theo},
 doi = {10.1007/978-3-030-11723-8}
}


@proceedings{CrimiAlessandroandBakasSpyridon.2021,
 abstract = {},
 year = {2021},
 title = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries},
 address = {Cham},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-030-72084-1},
 editor = {{Crimi, Alessandro and Bakas, Spyridon}}
}


@proceedings{CrimiAlessandroandBakasSpyridon.2022,
 abstract = {},
 year = {2022},
 title = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries},
 address = {Cham},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-031-08999-2},
 editor = {{Crimi, Alessandro and Bakas, Spyridon}}
}


@article{Csiszar.1975,
 abstract = {},
 author = {Csiszar, I.},
 year = {1975},
 title = {$I$-Divergence Geometry of Probability Distributions and Minimization Problems},
 volume = {3},
 number = {1},
 issn = {0091-1798},
 journal = {The Annals of Probability},
 doi = {10.1214/aop/1176996454}
}


@inproceedings{DebayanBhattacharya.2023,
 abstract = {},
 author = {Bhattacharya, Debayan and Behrendt, Finn and {Benjamin Tobias Becker} and {Dirk Beyersdorff} and {Elina Petersen} and {Marvin Petersen} and {Bastian Cheng} and {Dennis Eggert} and {Christian Betz} and {Anna Sophie Hoffmann} and Schlaefer, Alexander},
 title = {Unsupervised anomaly detection of paranasal anomalies in the maxillary sinus},
 keywords = {anomaly detection;autoencoder;paranasal anomaly;Unsupervised Anomaly Detection;VAE;variational autoencoder},
 pages = {124651B},
 volume = {12465},
 publisher = {SPIE},
 editor = {{Khan M. Iftekharuddin} and {Weijie Chen}},
 booktitle = {Medical Imaging 2023: Computer-Aided Diagnosis},
 year = {2023},
 doi = {10.1117/12.2651525}
}


@inproceedings{Dey.2021,
 abstract = {},
 author = {Dey, Raunak and Hong, Yi},
 title = {ASC-Net: Adversarial-Based Selective Network for Unsupervised Anomaly Segmentation},
 pages = {236--247},
 volume = {12905},
 publisher = {{Springer International Publishing}},
 editor = {de Bruijne, Marleen and Cattin, Philippe C. and Cotin, St{\'e}phane and Padoy, Nicolas and Speidel, Stefanie and Zheng, Yefeng and Essert, Caroline},
 booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2021},
 year = {2021},
 address = {Cham},
 doi = {10.1007/978-3-030-87240-3{\textunderscore }23}
}


@inproceedings{Dilokthanakul.2017,
 abstract = {We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models. We observe that the known problem of over-regularisation that has been shown to arise in regular VAEs also manifests itself in our model and leads to cluster degeneracy. We show that a heuristic called minimum information constraint that has been shown to mitigate this effect in VAEs can also be applied to improve unsupervised clustering performance with our model. Furthermore we analyse the effect of this heuristic and provide an intuition of the various processes with the help of visualizations. Finally, we demonstrate the performance of our model on synthetic data, MNIST and SVHN, showing that the obtained clusters are distinct, interpretable and result in achieving competitive performance on unsupervised clustering to the state-of-the-art results.},
 author = {Dilokthanakul, Nat and Mediano, Pedro A. M. and Garnelo, Marta and Lee, Matthew C. H. and Salimbeni, Hugh and Arulkumaran, Kai and Shanahan, Murray},
 title = {Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders},
 url = {https://arxiv.org/pdf/1611.02648},
 booktitle = {5th International Conference on Learning Representations, ICLR 2017},
 year = {2017},
 address = {Toulon, France}
}


@inproceedings{Doorenbos.2022,
 abstract = {We present an extension of the self-supervised outlier detection (SSD) framework~[12] to the three-dimensional case. We first apply contrastive learning on a network using a general dataset of two-dimensional slices randomly sampled from all the available...},
 author = {Doorenbos, Lars and Sznitman, Raphael and M{\'a}rquez-Neila, Pablo},
 title = {SS3D: Unsupervised Out-of-Distribution Detection and Localization for Medical Volumes},
 url = {https://link.springer.com/chapter/10.1007/978-3-030-97281-3_17},
 pages = {111--118},
 publisher = {{Springer, Cham}},
 year = {2022},
 doi = {10.1007/978-3-030-97281-3{\textunderscore }17}
}


@article{Drew.2013,
 abstract = {Researchers have shown that people often miss the occurrence of an unexpected yet salient event if they are engaged in a different task, a phenomenon known as inattentional blindness. However, demonstrations of inattentional blindness have typically involved naive observers engaged in an unfamiliar task. What about expert searchers who have spent years honing their ability to detect small abnormalities in specific types of images? We asked 24 radiologists to perform a familiar lung-nodule detection task. A gorilla, 48 times the size of the average nodule, was inserted in the last case that was presented. Eighty-three percent of the radiologists did not see the gorilla. Eye tracking revealed that the majority of those who missed the gorilla looked directly at its location. Thus, even expert searchers, operating in their domain of expertise, are vulnerable to inattentional blindness.},
 author = {Drew, Trafton and V{\~o}, Melissa L-H and Wolfe, Jeremy M.},
 year = {2013},
 title = {The invisible gorilla strikes again: sustained inattentional blindness in expert observers},
 pages = {1848--1853},
 volume = {24},
 number = {9},
 journal = {Psychological science},
 doi = {10.1177/0956797613479386}
}


@inproceedings{EloyRoura.2016,
 abstract = {SPIE Digital Library Proceedings},
 author = {Roura, Eloy and  Oliver, Arnau and  Cabezas, Mariano and  Valverde, Sergi and  Pareto, Deborah and  Vilanova, Joan and  Rami{\'o}-Torrent{\`a}, Llu{\'i}s and Rovira, {\`A}lex  and  Llad{\'o}, Xavier},
 title = {An SPM12 extension for multiple sclerosis lesion segmentation},
 url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9784/97842N/An-SPM12-extension-for-multiple-sclerosis-lesion-segmentation/10.1117/12.2216703.full},
 pages = {727--732},
 publisher = {SPIE},
 year = {2016},
 doi = {10.1117/12.2216703}
}


@inproceedings{EloyRoura.2016b,
 abstract = {},
 author = {Roura, Eloy and  Oliver Arnau  and Cabezas, Mariano and Valverde, Sergi  and Pareto, Deborah and Vilanova, Joan  and Rami{\'o}-Torrent{\`a}, Llu{\'i}s  and Rovira, {\`A}lex  and  Llad{\'o}, Xavier},
 title = {An SPM12 extension for multiple sclerosis lesion segmentation},
 pages = {97842N},
 volume = {9784},
 publisher = {SPIE},
 editor = {{Martin A. Styner} and {Elsa D. Angelini}},
 booktitle = {Medical Imaging 2016: Image Processing},
 year = {2016},
 doi = {10.1117/12.2216703}
}


@article{Falini.2022,
 abstract = {Brain tumors are abnormal cell growth in the brain tissues that can be cancerous or not. In any case, they could be a very aggressive disease that should be detected as early as possible. Usually, magnetic resonance imaging (MRI) is the main tool commonly adopted by neurologists and radiologists to identify and classify any possible anomalies present in the brain anatomy. In the present work, an automatic unsupervised method called Z2-\textgreek{g}, based on the use of adaptive finite-elements and suitable pre-processing and post-processing techniques, is introduced. The adaptive process, driven by a Zienkiewicz-Zhu type error estimator (Z2), is carried out on isotropic triangulations, while the given input images are pre-processed via nonlinear transformations (\textgreek{g} corrections) to enhance the ability of the error estimator to detect any relevant anomaly. The proposed methodology is able to automatically classify whether a given MR image represents a healthy or a diseased brain and, in this latter case, is able to locate the tumor area, which can be easily delineated by removing any redundancy with post-processing techniques based on morphological transformations. The method is tested on a freely available dataset achieving 0.846 of accuracy and F1 score equal to 0.88.},
 author = {Falini, Antonella},
 year = {2022},
 title = {Z2-\textgreek{g}: An Application of Zienkiewicz-Zhu Error Estimator to Brain Tumor Detection in MR Images},
 url = {https://www.mdpi.com/2313-433X/8/11/301},
 pages = {301},
 volume = {8},
 number = {11},
 issn = {2313-433X},
 journal = {Journal of Imaging},
 doi = {10.3390/jimaging8110301}
}


@article{Fernando.2020,
 abstract = {Machine learning-based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical application domains and we observe several similarities across these distinct applications. Despite this comparability, we observe a lack of structured organisation of these diverse research applications such that their advantages and limitations can be studied. The principal aim of this survey is to provide a thorough theoretical analysis of popular deep learning techniques in medical anomaly detection. In particular, we contribute a coherent and systematic review of state-of-the-art techniques, comparing and contrasting their architectural differences as well as training algorithms. Furthermore, we provide a comprehensive overview of deep model interpretation strategies that can be used to interpret model decisions. In addition, we outline the key limitations of existing deep medical anomaly detection techniques and propose key research directions for further investigation.},
 author = {Fernando, Tharindu and Gammulle, Harshala and Denman, Simon and Sridharan, Sridha and Fookes, Clinton},
 year = {2020},
 title = {Deep Learning for Medical Anomaly Detection -- A Survey},
 url = {http://arxiv.org/pdf/2012.02364v1},
 journal = {arXiv preprint 2012.02364v1}
}


@article{FlorentinBieder.2022,
 abstract = {Position Regression for Unsupervised Anomaly DetectionFlorentin Bieder,~Julia Wolleb,~Robin Sandk{\"u}hler,~Philippe C. CattinIn recent years, ano...},
 author = {Bieder, Florentin and Wolleb, Julia  and Sandk{\"u}hler, Robin  and Cattin, Philippe },
 year = {2022},
 title = {Position Regression for Unsupervised Anomaly Detection},
 url = {https://proceedings.mlr.press/v172/bieder22a.html},
 pages = {160--172},
 issn = {2640-3498},
 journal = {International Conference on Medical Imaging with Deep Learning}
}


@article{Galimzianova.2018,
 abstract = {Multiple sclerosis (MS) is a neurological disease characterized by focal lesions and morphological changes in the brain captured on magnetic resonance (MR) images. However, extraction of the corresponding imaging markers requires accurate segmentation of normal-appearing brain structures (NABS) and the lesions in MR images. On MR images of healthy brains, the NABS can be accurately captured by MR intensity mixture models, which, in combination with regularization techniques, such as in Markov random field (MRF) models, are known to give reliable NABS segmentation. However, on MR images that also contain abnormalities such as MS lesions, obtaining an accurate and reliable estimate of NABS intensity models is a challenge. We propose a method for automated segmentation of normal-appearing and abnormal structures in brain MR images that is based on a locally adaptive NABS model, a robust model parameters estimation method, and an MRF-based segmentation framework. Experiments on multisequence brain MR images of 30 MS patients show that, compared to whole-brain MR intensity model and compared to four popular unsupervised lesion segmentation methods, the proposed method increases the accuracy of MS lesion segmentation.},
 author = {Galimzianova, Alfiia and Lesjak, {\v{Z}}iga and Rubin, Daniel L. and Likar, Bo{\v{s}}tjan and Pernu{\v{s}}, Franjo and {\v{S}}piclin, {\v{Z}}iga},
 year = {2018},
 title = {Locally adaptive magnetic resonance intensity models for unsupervised segmentation of multiple sclerosis lesions},
 url = {https://pubmed.ncbi.nlm.nih.gov/29134190/},
 pages = {011007},
 volume = {5},
 number = {1},
 issn = {2329-4302},
 journal = {Journal of medical imaging (Bellingham, Wash.)},
 doi = {10.1117/1.JMI.5.1.011007}
}


@article{Glatz.2015,
 abstract = {Multifocal basal ganglia T2*-weighted (T2*w) hypointensities, which are believed to arise mainly from vascular mineralization, were recently proposed as a novel MRI biomarker for small vessel disease and ageing. These T2*w hypointensities are typically segmented semi-automatically, which is time consuming, associated with a high intra-rater variability and low inter-rater agreement. To address these limitations, we developed a fully automated, unsupervised segmentation method for basal ganglia T2*w hypointensities. This method requires conventional, co-registered T2*w and T1-weighted (T1w) volumes, as well as region-of-interest (ROI) masks for the basal ganglia and adjacent internal capsule generated automatically from T1w MRI. The basal ganglia T2*w hypointensities were then segmented with thresholds derived with an adaptive outlier detection method from respective bivariate T2*w/T1w intensity distributions in each ROI. Artefacts were reduced by filtering connected components in the initial masks based on their standardised T2*w intensity variance. The segmentation method was validated using a custom-built phantom containing mineral deposit models, i.e. gel beads doped with 3 different contrast agents in 7 different concentrations, as well as with MRI data from 98 community-dwelling older subjects in their seventies with a wide range of basal ganglia T2*w hypointensities. The method produced basal ganglia T2*w hypointensity masks that were in substantial volumetric and spatial agreement with those generated by an experienced rater (Jaccard index = 0.62 $\pm$ 0.40). These promising results suggest that this method may have use in automatic segmentation of basal ganglia T2*w hypointensities in studies of small vessel disease and ageing.},
 author = {Glatz, Andreas and Bastin, Mark E. and Kiker, Alexander J. and Deary, Ian J. and Wardlaw, Joanna M. and {Vald{\'e}s Hern{\'a}ndez}, Maria C.},
 year = {2015},
 title = {Automated segmentation of multifocal basal ganglia T2*-weighted MRI hypointensities},
 url = {https://www.sciencedirect.com/science/article/pii/S105381191400812X},
 pages = {332--346},
 volume = {105},
 issn = {1053-8119},
 journal = {NeuroImage},
 doi = {10.1016/j.neuroimage.2014.10.001}
}


@article{GM.2020,
 abstract = {},
 author = {GM, Harshvardhan and Gourisaria, Mahendra Kumar and Pandey, Manjusha and Rautaray, Siddharth Swarup},
 year = {2020},
 title = {A comprehensive survey and analysis of generative models in machine learning},
 pages = {100285},
 volume = {38},
 issn = {15740137},
 journal = {Computer Science Review},
 doi = {10.1016/j.cosrev.2020.100285}
}


@proceedings{Gooya.2018,
 abstract = {},
 year = {2018},
 title = {Simulation and Synthesis in Medical Imaging},
 address = {Cham},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-030-00536-8},
 editor = {Gooya, Ali and Goksel, Orcun and Oguz, Ipek and Burgos, Ninon}
}


@book{Greenspan.2023,
 abstract = {},
 year = {2023},
 title = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023},
 address = {Cham},
 publisher = {{Springer Nature Switzerland}},
 isbn = {978-3-031-43903-2},
 series = {Lecture Notes in Computer Science},
 editor = {Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell},
 doi = {10.1007/978-3-031-43904-9}
}


@proceedings{Greenspan.2023b,
 abstract = {},
 year = {2023},
 title = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023},
 address = {Cham},
 publisher = {{Springer Nature Switzerland}},
 isbn = {978-3-031-43904-9},
 editor = {Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell}
}


@book{Gullapalli.2011,
 abstract = {},
 author = {Gullapalli, Rao P.},
 year = {2011},
 title = {Investigation of Prognostic Ability of Novel Imaging Markers for Traumatic Brain Injury (TBI)},
 address = {Fort Belvoir, VA},
 publisher = {{Defense Technical Information Center}}
}


@article{Guo.2023,
 abstract = {Unsupervised anomaly detection (UAD) aims to recognize anomalous images based on the training set that contains only normal images. In medical image analysis, UAD benefits from leveraging the easily obtained normal (healthy) images, avoiding the costly collecting and labeling of anomalous (unhealthy) images. Most advanced UAD methods rely on frozen encoder networks pre-trained using ImageNet for extracting feature representations. However, the features extracted from the frozen encoders that are borrowed from natural image domains coincide little with the features required in the target medical image domain. Moreover, optimizing encoders usually causes pattern collapse in UAD. In this paper, we propose a novel UAD method, namely Encoder-Decoder Contrast (EDC), which optimizes the entire network to reduce biases towards pre-trained image domain and orient the network in the target medical domain. We start from feature reconstruction approach that detects anomalies from reconstruction errors. Essentially, a contrastive learning paradigm is introduced to tackle the problem of pattern collapsing while optimizing the encoder and the reconstruction decoder simultaneously. In addition, to prevent instability and further improve performances, we propose to bring globality into the contrastive objective function. Extensive experiments are conducted across four medical image modalities including optical coherence tomography, color fundus image, brain MRI, and skin lesion image, where our method outperforms all current state-of-the-art UAD methods. Code is available at: https://github.com/guojiajeremy/EDC.},
 author = {Guo, Jia and Lu, Shuai and Jia, Lize and Zhang, Weihang and Li, Huiqi},
 year = {2023},
 title = {Encoder-Decoder Contrast for Unsupervised Anomaly Detection in Medical Images},
 volume = {PP},
 issn = {0278-0062},
 journal = {IEEE transactions on medical imaging},
 doi = {10.1109/TMI.2023.3327720}
}


@article{Han.2021,
 abstract = {BACKGROUND

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.

RESULTS

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 [Formula: see text] loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average [Formula: see text] loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 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.

CONCLUSIONS

Similar to physicians' way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen healthy images. As the first unsupervised various disease diagnosis, MADGAN can reliably detect the accumulation of subtle anatomical anomalies and hyper-intense enhancing lesions, such as (especially late-stage) AD and brain metastases on multi-sequence MRI scans.},
 author = {Han, Changhee and Rundo, Leonardo and Murao, Kohei and Noguchi, Tomoyuki and Shimahara, Yuki and Milacski, Zolt{\'a}n {\'A}d{\'a}m and Koshino, Saori and Sala, Evis and Nakayama, Hideki and Satoh, Shin'ichi},
 year = {2021},
 title = {MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction},
 pages = {31},
 volume = {22},
 number = {Suppl 2},
 journal = {BMC bioinformatics},
 doi = {10.1186/s12859-020-03936-1}
}


@inproceedings{He.2016,
 abstract = {},
 author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
 title = {Deep Residual Learning for Image Recognition},
 pages = {770--778},
 publisher = {IEEE},
 isbn = {978-1-4673-8851-1},
 booktitle = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 year = {2016},
 address = {Las Vegas, NV, USA},
 doi = {10.1109/CVPR.2016.90}
}


@proceedings{Heinrich.2021,
 abstract = {},
 year = {2021},
 title = {Proceedings of the Fourth Conference on Medical Imaging with Deep Learning},
 publisher = {PMLR},
 series = {Proceedings of Machine Learning Research},
 editor = {Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schl{\"a}fer, Alexander and Ernst, Floris}
}


@article{Ho.2020,
 abstract = {},
 author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
 year = {2020},
 title = {Denoising diffusion probabilistic models},
 pages = {6840--6851},
 volume = {33},
 journal = {Advances in neural information processing systems}
}


@inproceedings{Huang.2017,
 abstract = {Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections - one between each layer and its subsequent layer - our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet .},
 author = {Huang, Gao and Liu, Zhuang and {van der Maaten}, Laurens and Weinberger, Kilian Q.},
 title = {Densely Connected Convolutional Networks},
 url = {https://arxiv.org/pdf/1608.06993},
 pages = {2261--2269},
 booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 year = {2017},
 address = {Honolulu, HI, USA}
}


@article{Hughes.2017,
 abstract = {PURPOSE

The goal of the Developing Human Connectome Project is to acquire MRI in 1000 neonates to create a dynamic map of human brain connectivity during early development. High-quality imaging in this cohort without sedation presents a number of technical and practical challenges.

METHODS

We designed a neonatal brain imaging system (NBIS) consisting of a dedicated 32-channel receive array coil and a positioning device that allows placement of the infant's head deep into the coil for maximum signal-to-noise ratio (SNR). Disturbance to the infant was minimized by using an MRI-compatible trolley to prepare and transport the infant and by employing a slow ramp-up and continuation of gradient noise during scanning. Scan repeats were minimized by using a restart capability for diffusion MRI and retrospective motion correction. We measured the 1) SNR gain, 2) number of infants with a completed scan protocol, and 3) number of anatomical images with no motion artifact using NBIS compared with using an adult 32-channel head coil.

RESULTS

The NBIS has 2.4 times the SNR of the adult coil and 90{\%} protocol completion rate.

CONCLUSION

The NBIS allows advanced neonatal brain imaging techniques to be employed in neonatal brain imaging with high protocol completion rates. Magn Reson Med 78:794-804, 2017. {\copyright} 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.},
 author = {Hughes, Emer J. and Winchman, Tobias and Padormo, Francesco and Teixeira, Rui and Wurie, Julia and Sharma, Maryanne and Fox, Matthew and Hutter, Jana and Cordero-Grande, Lucilio and Price, Anthony N. and Allsop, Joanna and Bueno-Conde, Jose and Tusor, Nora and Arichi, Tomoki and Edwards, A. D. and Rutherford, Mary A. and Counsell, Serena J. and Hajnal, Joseph V.},
 year = {2017},
 title = {A dedicated neonatal brain imaging system},
 keywords = {Brain/diagnostic imaging;Connectome/instrumentation/methods;Humans;Infant, Newborn/physiology;Magnetic Resonance Imaging/instrumentation/methods;Neuroimaging/instrumentation/methods;Signal-To-Noise Ratio},
 pages = {794--804},
 volume = {78},
 number = {2},
 journal = {Magnetic resonance in medicine},
 doi = {10.1002/mrm.26462}
}


@proceedings{IEEEComputerSociety.2020,
 abstract = {},
 year = {2020},
 title = {2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
 institution = {{IEEE Computer Society}}
}


@proceedings{IEEEConferenceonComputerVisionandPatternRecognition.2016,
 abstract = {},
 year = {2016},
 title = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR): CVPR 2016 : proceedings : 26 June-1 July 2016, Las Vegas, Nevada},
 keywords = {Maschinelles Sehen;Mustererkennung},
 address = {Las Vegas, NV, USA},
 publisher = {IEEE},
 isbn = {978-1-4673-8851-1},
 institution = {{IEEE Conference on Computer Vision and Pattern Recognition} and {Institute of Electrical and Electronics Engineers} and CVPR}
}


@article{Iglesias.2011,
 abstract = {Automatic whole-brain extraction from magnetic resonance images (MRI), also known as skull stripping, is a key component in most neuroimage pipelines. As the first element in the chain, its robustness is critical for the overall performance of the system. Many skull stripping methods have been proposed, but the problem is not considered to be completely solved yet. Many systems in the literature have good performance on certain datasets (mostly the datasets they were trained/tuned on), but fail to produce satisfactory results when the acquisition conditions or study populations are different. In this paper we introduce a robust, learning-based brain extraction system (ROBEX). The method combines a discriminative and a generative model to achieve the final result. The discriminative model is a Random Forest classifier trained to detect the brain boundary; the generative model is a point distribution model that ensures that the result is plausible. When a new image is presented to the system, the generative model is explored to find the contour with highest likelihood according to the discriminative model. Because the target shape is in general not perfectly represented by the generative model, the contour is refined using graph cuts to obtain the final segmentation. Both models were trained using 92 scans from a proprietary dataset but they achieve a high degree of robustness on a variety of other datasets. ROBEX was compared with six other popular, publicly available methods (BET, BSE, FreeSurfer, AFNI, BridgeBurner, and GCUT) on three publicly available datasets (IBSR, LPBA40, and OASIS, 137 scans in total) that include a wide range of acquisition hardware and a highly variable population (different age groups, healthy/diseased). The results show that ROBEX provides significantly improved performance measures for almost every method/dataset combination.},
 author = {Iglesias, Juan Eugenio and Liu, Cheng-Yi and Thompson, Paul M. and Tu, Zhuowen},
 year = {2011},
 title = {Robust brain extraction across datasets and comparison with publicly available methods},
 keywords = {Adult;Aged;Algorithms;Brain/anatomy {\&} histology/pathology;Computer Simulation;Database Management Systems;Databases, Factual;Discriminant Analysis;Electronic Data Processing;Female;Humans;Image Processing, Computer-Assisted/methods;Magnetic Resonance Imaging/methods;Male;Middle Aged;Models, Anatomic;Pattern Recognition, Automated/methods;Reproducibility of Results;Sensitivity and Specificity;Skull/anatomy {\&} histology},
 pages = {1617--1634},
 volume = {30},
 number = {9},
 issn = {0278-0062},
 journal = {IEEE transactions on medical imaging},
 doi = {10.1109/TMI.2011.2138152}
}


@incollection{Iqbal.2023,
 abstract = {},
 author = {Iqbal, Hasan and Khalid, Umar and Chen, Chen and Hua, Jing},
 title = {Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model},
 pages = {372--381},
 volume = {14348},
 publisher = {{Springer Nature Switzerland}},
 isbn = {978-3-031-45672-5},
 series = {Lecture Notes in Computer Science},
 editor = {Cao, Xiaohuan and Xu, Xuanang and Rekik, Islem and Cui, Zhiming and Ouyang, Xi},
 booktitle = {Machine Learning in Medical Imaging},
 year = {2023},
 address = {Cham},
 doi = {10.1007/978-3-031-45673-2{\textunderscore }37}
}


@article{Isensee.2019,
 abstract = {Brain extraction is a critical preprocessing step in the analysis of neuroimaging studies conducted with magnetic resonance imaging (MRI) and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aim to overcome these limitations. We demonstrate that HD-BET outperforms six popular, publicly available brain extraction algorithms in several large-scale neuroimaging datasets, including one from a prospective multicentric trial in neuro-oncology, yielding state-of-the-art performance with median improvements of +1.16 to +2.50 points for the Dice coefficient and -0.66 to -2.51 mm for the Hausdorff distance. Importantly, the HD-BET algorithm, which shows robust performance in the presence of pathology or treatment-induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility, the HD-BET prediction algorithm is made freely available (www.neuroAI-HD.org) and may become an essential component for robust, automated, high-throughput processing of MRI neuroimaging data.},
 author = {Isensee, Fabian and Schell, Marianne and Pflueger, Irada and Brugnara, Gianluca and Bonekamp, David and Neuberger, Ulf and Wick, Antje and Schlemmer, Heinz-Peter and Heiland, Sabine and Wick, Wolfgang and Bendszus, Martin and Maier-Hein, Klaus H. and Kickingereder, Philipp},
 year = {2019},
 title = {Automated brain extraction of multisequence MRI using artificial neural networks},
 pages = {4952--4964},
 volume = {40},
 number = {17},
 issn = {1097-0193},
 journal = {Human brain mapping},
 doi = {10.1002/hbm.24750}
}


@inproceedings{JacobC.Reinhold.2020,
 abstract = {SPIE Digital Library Proceedings},
 author = {Reinhold,Jacob  and He, Yufan and Han, Shizhong and Chen, Yunqiang and Gao, Dashan and Lee, Junghoon and Prince, Jerry and Carass, Aaron},
 title = {Finding novelty with uncertainty},
 url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11313/113130H/Finding-novelty-with-uncertainty/10.1117/12.2549341.full},
 pages = {82--87},
 publisher = {SPIE},
 year = {2020},
 doi = {10.1117/12.2549341}
}


@article{Jalalifar.2020,
 abstract = {Accurate segmentation of brain tumors is a challenging task and also a crucial step in diagnosis and treatment planning for cancer patients. Magnetic resonance imaging (MRI) is the standard imaging modality for detection, characterization, treatment planning and outcome evaluation of brain tumors. MRI scans are usually acquired at multiple sessions before and after the treatment. An automatic segmentation framework is highly desirable to segment brain tumors in MR images as it streamlines the image-guided radiation therapy workflow considerably. Automatic segmentation of brain tumors also facilitates an incremental development of data-driven systems for therapy outcome prediction based on radiomics analysis. In this study, an outlier-detection-based segmentation framework is proposed to delineate brain tumors in magnetic resonance (MR) images automatically. The proposed method considers the tumor and edema pixels in an MR image as outliers compared to the pixels associated with the healthy tissue. The framework generates two outlier masks using independent one-class support vector machines that operate on post-contrast T1-weighted (T1w) and T2-weighted-fluid-attenuation-inversion-recovery (T2-FLAIR) images. The outlier masks are subsequently refined and fused using a number of morphological and logical operators to estimate a tumor mask for each image slice. The framework was constructed and evaluated using the MRI data acquired from 35 and 5 patients with brain metastasis, respectively. The obtained results demonstrated an average Dice similarity coefficient and Hausdorff distance of 0.84 $\pm$ 0.06 and 1.85 $\pm$ 0.48 mm, respectively, between the manual (ground truth) and automatic tumor contours, on the independent test set.},
 author = {Jalalifar, Ali and Soliman, Hany and Ruschin, Mark and Sahgal, Arjun and Sadeghi-Naini, Ali},
 year = {2020},
 title = {A Brain Tumor Segmentation Framework Based on Outlier Detection Using One-Class Support Vector Machine},
 pages = {1067--1070},
 volume = {2020},
 issn = {2694-0604},
 journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
 doi = {10.1109/EMBC44109.2020.9176263}
}


@inproceedings{JohnO.Glass.1998,
 abstract = {SPIE Digital Library Proceedings},
 author = {Glass, John and Reddick, Wilburn and Yo, Virginia  and Steen, Grant },
 title = {Segmentation of inversion recovery MR images using neural networks: a study on aging},
 url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/3338/1/Segmentation-of-inversion-recovery-MR-images-using-neural-networks/10.1117/12.310938.full},
 pages = {597--607},
 publisher = {SPIE},
 year = {1998},
 doi = {10.1117/12.310938}
}


@article{Johnson.2019,
 abstract = {The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g., fraud detection and cancer detection. Moreover, highly imbalanced data poses added difficulty, as most learners will exhibit bias towards the majority class, and in extreme cases, may ignore the minority class altogether. Class imbalance has been studied thoroughly over the last two decades using traditional machine learning models, i.e. non-deep learning. Despite recent advances in deep learning, along with its increasing popularity, very little empirical work in the area of deep learning with class imbalance exists. Having achieved record-breaking performance results in several complex domains, investigating the use of deep neural networks for problems containing high levels of class imbalance is of great interest. Available studies regarding class imbalance and deep learning are surveyed in order to better understand the efficacy of deep learning when applied to class imbalanced data. This survey discusses the implementation details and experimental results for each study, and offers additional insight into their strengths and weaknesses. Several areas of focus include: data complexity, architectures tested, performance interpretation, ease of use, big data application, and generalization to other domains. We have found that research in this area is very limited, that most existing work focuses on computer vision tasks with convolutional neural networks, and that the effects of big data are rarely considered. Several traditional methods for class imbalance, e.g. data sampling and cost-sensitive learning, prove to be applicable in deep learning, while more advanced methods that exploit neural network feature learning abilities show promising results. The survey concludes with a discussion that highlights various gaps in deep learning from class imbalanced data for the purpose of guiding future research.},
 author = {Johnson, Justin M. and Khoshgoftaar, Taghi M.},
 year = {2019},
 title = {Survey on deep learning with class imbalance},
 pages = {1--54},
 volume = {6},
 number = {1},
 issn = {2196-1115},
 journal = {Journal of Big Data},
 doi = {10.1186/s40537-019-0192-5}
}


@inproceedings{JulioVillalonReina.2013,
 abstract = {SPIE Digital Library Proceedings},
 author = {Villalon-Reina, Julio and Gutierrez-Carvajal, Ricardo  and Thompson, Paul  and Romero-Castro, Eduardo },
 title = {detecting multiple sclerosis lesions with a fully bioinspired visual attention model},
 url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/8922/89220A/detecting-multiple-sclerosis-lesions-with-a-fully-bioinspired-visual-attention/10.1117/12.2042046.full},
 pages = {72--81},
 publisher = {SPIE},
 year = {2013},
 doi = {10.1117/12.2042046}
}


@article{Jumiawi.2022,
 abstract = {Computer vision plays an important role in the accurate foreground detection of medical images. Diagnosing diseases in their early stages has effective life-saving potential, and this is every physician's goal. There is a positive relationship between improving image segmentation methods and precise diagnosis in medical images. This relation provides a profound indication for feature extraction in a segmented image, such that an accurate separation occurs between the foreground and the background. There are many thresholding-based segmentation methods found under the pure image processing approach. Minimum cross entropy thresholding (MCET) is one of the frequently used mean-based thresholding methods for medical image segmentation. In this paper, the aim was to boost the efficiency of MCET, based on heterogeneous mean filter approaches. The proposed model estimates an optimized mean by excluding the negative influence of noise, local outliers, and gray intensity levels; thus, obtaining new mean values for the MCET's objective function. The proposed model was examined compared to the original and related methods, using three types of medical image dataset. It was able to show accurate results based on the performance measures, using the benchmark of unsupervised and supervised evaluation.},
 author = {Jumiawi, Walaa Ali H. and El-Zaart, Ali},
 year = {2022},
 title = {A Boosted Minimum Cross Entropy Thresholding for Medical Images Segmentation Based on Heterogeneous Mean Filters Approaches},
 url = {https://www.mdpi.com/2313-433X/8/2/43},
 pages = {43},
 volume = {8},
 number = {2},
 issn = {2313-433X},
 journal = {Journal of Imaging},
 doi = {10.3390/jimaging8020043}
}


@article{Jumiawi.2022b,
 abstract = {Mean-based thresholding methods are among the most popular techniques that are used for images segmentation. Thresholding is a fundamental process for many applications since it provides a good degree of intensity separation of given images. Minimum cross-entropy thresholding (MCET) is one of the widely used mean-based methods for images segmentation; it is based on a classical mean that remains steady and limited value. In this paper, to improve the efficiency of MCET, dedicated mean estimation approaches are proposed to be used with MCET, instead of using the classical mean. The proposed mean estimation approaches, for example, alpha trim, harmonic, contraharmonic, and geometric, tend to exclude the negative impact of the undesired parts from the mean computation process, such as noises, local outliers, and gray intensity levels, and then provide an improvement for the thresholding process that can reflect good segmentation results. The proposed technique adds a profound impact on accurate images segmentation. It can be extended to other applications in object detection. Three data sets of medical images were applied for segmentation in this paper, including magnetic resonance imaging (MRI) Alzheimer's, MRI brain tumor, and skin lesion. The unsupervised and supervised evaluations were used to conduct the efficiency of the proposed method.},
 author = {Jumiawi, Walaa Ali H. and El-Zaart, Ali},
 year = {2022},
 title = {Improving Minimum Cross-Entropy Thresholding for Segmentation of Infected Foregrounds in Medical Images Based on Mean Filters Approaches},
 url = {https://www.hindawi.com/journals/cmmi/2022/9289574/},
 pages = {9289574},
 volume = {2022},
 issn = {1555-4309},
 journal = {Contrast Media {\&} Molecular Imaging},
 doi = {10.1155/2022/9289574}
}


@article{Kabasawa.2022,
 abstract = {Clinical MRI systems have continually improved over the years since their introduction in the 1980s. In MRI technical development, the developments in each MRI system component, including data acquisition, image reconstruction, and hardware systems, have impacted the others. Progress in each component has induced new technology development opportunities in other components. New technologies outside of the MRI field, for example, computer science, data processing, and semiconductors, have been immediately incorporated into MRI development, which resulted in innovative applications. With high performance computing and MR technology innovations, MRI can now provide large volumes of functional and anatomical image datasets, which are important tools in various research fields. MRI systems are now combined with other modalities, such as positron emission tomography (PET) or therapeutic devices. These hybrid systems provide additional capabilities.In this review, MRI advances in the last two decades will be considered. We will discuss the progress of MRI systems, the enabling technology, established applications, current trends, and the future outlook.},
 author = {Kabasawa, Hiroyuki},
 year = {2022},
 title = {MR Imaging in the 21st Century: Technical Innovation over the First Two Decades},
 keywords = {Image Processing, Computer-Assisted/methods;Inventions;Magnetic Resonance Imaging/methods;Positron-Emission Tomography},
 pages = {71--82},
 volume = {21},
 number = {1},
 journal = {Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine},
 doi = {10.2463/mrms.rev.2021-0011}
}


@article{Karayiannis.1999,
 abstract = {This paper evaluates a segmentation technique for magnetic resonance (MR) images of the brain based on fuzzy algorithms for learning vector quantization (FALVQ). These algorithms perform vector quantization by updating all prototypes of a competitive network through an unsupervised learning process. Segmentation of MR images is formulated as an unsupervised vector quantization process, where the local values of different relaxation parameters form the feature vectors which are represented by a relatively small set of prototypes. The experiments evaluate a variety of FALVQ algorithms in terms of their ability to identify different tissues and discriminate between normal tissues and abnormalities.},
 author = {Karayiannis, N. B. and Pai, P. I.},
 year = {1999},
 title = {Segmentation of magnetic resonance images using fuzzy algorithms for learning vector quantization},
 pages = {172--180},
 volume = {18},
 number = {2},
 issn = {1558-254X},
 journal = {IEEE Transactions on Medical Imaging},
 doi = {10.1109/42.759126}
}


@proceedings{KarenDrukker.2022,
 abstract = {},
 year = {2022},
 title = {Medical Imaging 2022: Computer-Aided Diagnosis},
 publisher = {SPIE},
 editor = {{Karen Drukker} and {Khan M. Iftekharuddin}},
 institution = {{International Society for Optics and Photonics}}
}


@inproceedings{Kascenas.2022,
 abstract = {},
 author = {Kascenas, Antanas and Young, Rory and Jensen, Bjorn Sand and Pugeault, Nicolas and O'Neil, Alison Q.},
 title = {Anomaly Detection via Context and Local Feature Matching},
 pages = {1--5},
 booktitle = {2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)},
 year = {2022},
 doi = {10.1109/ISBI52829.2022.9761524}
}


@inproceedings{Kascenas.2022b,
 abstract = {Pathological brain lesions exhibit diverse appearance in brain images, making it difficult to train supervised detection solutions due to the lack of comprehensive data and annotations. Thus, in this work we tackle unsupervised anomaly detection, using only healthy data for training with the aim of detecting unseen anomalies at test time. Many current approaches employ autoencoders with restrictive architectures (i.e. containing information bottlenecks) that tend to give poor reconstructions of not only the anomalous but also the normal parts of the brain. Instead, we investigate classical denoising autoencoder models that do not require bottlenecks and can employ skip connections to give high resolution fidelity. We design a simple noise generation method of upscaling low-resolution noise that enables high-quality reconstructions. We find that with appropriate noise generation, denoising autoencoder reconstruction errors generalize to hyperintense lesion segmentation and reach state of the art performance for unsupervised tumor detection in brain MRI data, beating more complex methods such as variational autoencoders. We believe this provides a strong and easy-to-implement baseline for further research into unsupervised anomaly detection.},
 author = {Kascenas, Antanas and Pugeault, Nicolas and O'Neil, Alison Q.},
 title = {Denoising Autoencoders for Unsupervised Anomaly Detection in Brain MRI},
 url = {https://proceedings.mlr.press/v172/kascenas22a.html},
 pages = {653--664},
 volume = {172},
 publisher = {PMLR},
 series = {Proceedings of Machine Learning Research},
 editor = {Konukoglu, Ender and Menze, Bjoern and Venkataraman, Archana and Baumgartner, Christian and Dou, Qi and Albarqouni, Shadi},
 booktitle = {Proceedings of The 5th International Conference on Medical Imaging with Deep Learning},
 year = {2022}
}


@article{Kascenas.2023,
 abstract = {Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes, with similar noise parameter adjustments giving good performance for both DAEs and diffusion models. Visual inspection of the reconstructions suggests that the training noise influences the trade-off between the extent of the detail that is reconstructed and the extent of erasure of anomalies, both of which contribute to better anomaly detection performance. We validate our findings on two real-world datasets (tumor detection in brain MRI and hemorrhage/ischemia/tumor detection in brain CT), showing good detection on diverse anomaly appearances. Overall, we find that a DAE trained with coarse noise is a fast and simple method that gives state-of-the-art accuracy. Diffusion models applied to anomaly detection are as yet in their infancy and provide a promising avenue for further research. Code for our DAE model and coarse noise is provided at: https://github.com/AntanasKascenas/DenoisingAE.},
 author = {Kascenas, Antanas and Sanchez, Pedro and Schrempf, Patrick and Wang, Chaoyang and Clackett, William and Mikhael, Shadia S. and Voisey, Jeremy P. and Goatman, Keith and Weir, Alexander and Pugeault, Nicolas and Tsaftaris, Sotirios A. and O'Neil, Alison Q.},
 year = {2023},
 title = {The role of noise in denoising models for anomaly detection in medical images},
 pages = {102963},
 volume = {90},
 issn = {1361-8415},
 journal = {Medical Image Analysis},
 doi = {10.1016/j.media.2023.102963}
}


@proceedings{KhanM.Iftekharuddin.2023,
 abstract = {},
 year = {2023},
 title = {Medical Imaging 2023: Computer-Aided Diagnosis},
 publisher = {SPIE},
 editor = {{Khan M. Iftekharuddin} and {Weijie Chen}},
 institution = {{International Society for Optics and Photonics}}
}


@inproceedings{Kingma.2014,
 abstract = {},
 author = {Kingma, Diederik and Welling, Max},
 title = {Auto-Encoding Variational Bayes},
 url = {https://arxiv.org/pdf/1312.6114.pdf},
 series = {ICLR 2014},
 editor = {Bengio, Yoshua and LeCun, Yann},
 booktitle = {2nd International Conference on Learning Representations},
 year = {2014},
 address = {Banff, AB, Canada}
}


@proceedings{Konukoglu.2022,
 abstract = {},
 year = {2022},
 title = {Proceedings of The 5th International Conference on Medical Imaging with Deep Learning},
 publisher = {PMLR},
 series = {Proceedings of Machine Learning Research},
 editor = {Konukoglu, Ender and Menze, Bjoern and Venkataraman, Archana and Baumgartner, Christian and Dou, Qi and Albarqouni, Shadi}
}


@article{Kuijf.2019,
 abstract = {},
 author = {Kuijf, Hugo J. and Biesbroek, J. Matthijs and de Bresser, Jeroen and Heinen, Rutger and Andermatt, Simon and Bento, Mariana and Berseth, Matt and Belyaev, Mikhail and Cardoso, M. Jorge and Casamitjana, Adria and others},
 year = {2019},
 title = {Standardized assessment of automatic segmentation of white matter hyperintensities and results of the WMH segmentation challenge},
 pages = {2556--2568},
 volume = {38},
 number = {11},
 issn = {0278-0062},
 journal = {IEEE transactions on medical imaging}
}


@article{Lagogiannis.2023,
 abstract = {Deep unsupervised approaches are gathering increased attention for applications such as pathology detection and segmentation in medical images since they promise to alleviate the need for large labeled datasets and are more generalizable than their supervised counterparts in detecting any kind of rare pathology. As the Unsupervised Anomaly Detection (UAD) literature continuously grows and new paradigms emerge, it is vital to continuously evaluate and benchmark new methods in a common framework, in order to reassess the state-of-the-art (SOTA) and identify promising research directions. To this end, we evaluate a diverse selection of cutting-edge UAD methods on multiple medical datasets, comparing them against the established SOTA in UAD for brain MRI. Our experiments demonstrate that newly developed feature-modeling methods from the industrial and medical literature achieve increased performance compared to previous work and set the new SOTA in a variety of modalities and datasets. Additionally, we show that such methods are capable of benefiting from recently developed self-supervised pre-training algorithms, further increasing their performance. Finally, we perform a series of experiments in order to gain further insights into some unique characteristics of selected models and datasets. Our code can be found under https://github.com/iolag/UPD{\_}study/.},
 author = {Lagogiannis, Ioannis and Meissen, Felix and Kaissis, Georgios and Rueckert, Daniel},
 year = {2023},
 title = {Unsupervised Pathology Detection: A Deep Dive Into the State of the Art},
 volume = {PP},
 issn = {0278-0062},
 journal = {IEEE transactions on medical imaging},
 doi = {10.1109/TMI.2023.3298093}
}


@inproceedings{Lambert.2021,
 abstract = {},
 author = {Lambert, Benjamin and Louis, Maxime and Doyle, Senan and Forbes, Florence and Dojat, Michel and Tucholka, Alan},
 title = {Leveraging 3d Information In Unsupervised Brain Mri Segmentation},
 pages = {187--190},
 booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
 year = {2021},
 doi = {10.1109/ISBI48211.2021.9433894}
}


@article{LaMontagne.2018,
 abstract = {},
 author = {LaMontagne, Pamela J. and Keefe, Sarah and Lauren, Wallace and Xiong, Chengjie and Grant, Elizabeth A. and Moulder, Krista L. and Morris, John C. and Benzinger, Tammie L.S. and Marcus, Daniel S.},
 year = {2018},
 title = {OASIS-3: Longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer's disease},
 pages = {P1097-P1097},
 volume = {14},
 journal = {Alzheimer's {\&} Dementia}
}


@article{LapuyadeLahorgue.2017,
 abstract = {Nowadays, multi-source image acquisition attracts an increasing interest in many fields, such as multi-modal medical image segmentation. Such acquisition aims at considering complementary information to perform image segmentation, since the same scene has been observed by various types of images. However, strong dependence often exists between multi-source images. This dependence should be taken into account when we try to extract joint information for precisely making a decision. In order to statistically model this dependence between multiple sources, we propose a novel multi-source fusion method based on the Gaussian copula. The proposed fusion model is integrated in a statistical framework with the hidden Markov field inference in order to delineate a target volume from multi-source images. Estimation of parameters of the models and segmentation of the images are jointly performed by an iterative algorithm based on Gibbs sampling. Experiments are performed on multi-sequence MRI to segment tumors. The results show that the proposed method based on the Gaussian copula is effective to accomplish multi-source image segmentation.},
 author = {Lapuyade-Lahorgue, Jerome and Xue, Jing-Hao and Ruan, Su},
 year = {2017},
 title = {Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions},
 pages = {3187--3195},
 volume = {26},
 number = {7},
 issn = {1941-0042},
 journal = {IEEE Transactions on Image Processing},
 doi = {10.1109/TIP.2017.2685345}
}


@article{LapuyadeLahorgue.2022,
 abstract = {Purpose: Multisource images are interesting in medical imaging. Indeed, multisource images enable the use of complementary information of different sources such as for T1 and T2 modalities in MRI imaging. However, such multisource data can also be subject to redundancy and correlation. The question is how to efficiently fuse the multisource information without reinforcing the redundancy. We propose a method for segmenting multisource images that are statistically correlated. Approach: The method that we propose is the continuation of a prior work in which we introduce the copula model in hidden Markov fields (HMF). To achieve the multisource segmentations, we use a functional measure of dependency called {\textquotedbl}copula.{\textquotedbl} This copula is incorporated in the conditionally random fields (CRF). Contrary to HMF, where we consider a prior knowledge on the hidden states modeled by an HMF, in CRF, there is no prior information and only the distribution of the hidden states conditionally to the observations can be known. This conditional distribution depends on the data and can be modeled by an energy function composed of two terms. The first one groups the voxels having similar intensities in the same class. As for the second term, it encourages a pair of voxels to be in the same class if the difference between their intensities is not too big. Results: A comparison between HMF and CRF is performed via theory and experimentations using both simulated and real data from BRATS 2013. Moreover, our method is compared with different state-of-the-art methods, which include supervised (convolutional neural networks) and unsupervised (hierarchical MRF). Our unsupervised method gives similar results as decision trees for synthetic images and as convolutional neural networks for real images; both methods are supervised. Conclusions: We compare two statistical methods using the copula: HMF and CRF to deal with multicorrelated images. We demonstrate the interest of using copula. In both models, the copula considerably improves the results compared with individual segmentations.},
 author = {Lapuyade-Lahorgue, J{\'e}r{\^o}me and Ruan, Su},
 year = {2022},
 title = {Segmentation of multicorrelated images with copula models and conditionally random fields},
 url = {https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-9/issue-01/014001/Segmentation-of-multicorrelated-images-with-copula-models-and-conditionally-random/10.1117/1.JMI.9.1.014001.full},
 pages = {014001},
 volume = {9},
 number = {1},
 issn = {2329-4302},
 journal = {Journal of medical imaging (Bellingham, Wash.)},
 doi = {10.1117/1.JMI.9.1.014001}
}


@article{Lavda.2020,
 abstract = {One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. This is primarily due to the use of a simple isotropic Gaussian as the prior for the latent code in the ancestral sampling procedure for data generations. In this paper, we propose a novel formulation of variational autoencoders, conditional prior VAE (CP-VAE), with a two-level generative process for the observed data where continuous z and a discrete c variables are introduced in addition to the observed variables x. By learning data-dependent conditional priors, the new variational objective naturally encourages a better match between the posterior and prior conditionals, and the learning of the latent categories encoding the major source of variation of the original data in an unsupervised manner. Through sampling continuous latent code from the data-dependent conditional priors, we are able to generate new samples from the individual mixture components corresponding, to the multimodal structure over the original data. Moreover, we unify and analyse our objective under different independence assumptions for the joint distribution of the continuous and discrete latent variables. We provide an empirical evaluation on one synthetic dataset and three image datasets, FashionMNIST, MNIST, and Omniglot, illustrating the generative performance of our new model comparing to multiple baselines.},
 author = {Lavda, Frantzeska and Gregorov{\'a}, Magda and Kalousis, Alexandros},
 year = {2020},
 title = {Data-Dependent Conditional Priors for Unsupervised Learning of Multimodal Data},
 volume = {22},
 number = {8},
 journal = {Entropy (Basel, Switzerland)},
 doi = {10.3390/e22080888}
}


@article{Lee.2018,
 abstract = {},
 author = {Lee, Kimin and Lee, Kibok and Lee, Honglak and Shin, Jinwoo},
 year = {2018},
 title = {A simple unified framework for detecting out-of-distribution samples and adversarial attacks},
 volume = {31},
 journal = {Advances in neural information processing systems}
}


@article{Lesjak.2018,
 abstract = {},
 author = {Lesjak, {\v{Z}}iga and Galimzianova, Alfiia and Koren, Ale{\v{s}} and Lukin, Matej and Pernu{\v{s}}, Franjo and Likar, Bo{\v{s}}tjan and {\v{S}}piclin, {\v{Z}}iga},
 year = {2018},
 title = {A novel public MR image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus},
 pages = {51--63},
 volume = {16},
 number = {1},
 journal = {Neuroinformatics}
}


@inproceedings{Li.2021,
 abstract = {},
 author = {Li, Chun-Liang and Sohn, Kihyuk and Yoon, Jinsung and Pfister, Tomas},
 title = {Cutpaste: Self-supervised learning for anomaly detection and localization},
 pages = {9664--9674},
 booktitle = {Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
 year = {2021}
}


@article{Liew.2022,
 abstract = {},
 author = {Liew, Sook-Lei and Lo, Bethany P. and Donnelly, Miranda R. and Zavaliangos-Petropulu, Artemis and Jeong, Jessica N. and Barisano, Giuseppe and Hutton, Alexandre and Simon, Julia P. and Juliano, Julia M. and Suri, Anisha and others},
 year = {2022},
 title = {A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms},
 pages = {320},
 volume = {9},
 number = {1},
 journal = {Scientific data}
}


@article{Lundervold.2019,
 abstract = {What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.},
 author = {Lundervold, Alexander Selvikv{\aa}g and Lundervold, Arvid},
 year = {2019},
 title = {An overview of deep learning in medical imaging focusing on MRI},
 pages = {102--127},
 volume = {29},
 number = {2},
 journal = {Zeitschrift fur medizinische Physik},
 doi = {10.1016/j.zemedi.2018.11.002}
}


@article{Luo.2023,
 abstract = {PURPOSE

To develop a general unsupervised anomaly detection method based only on MR images of normal brains to automatically detect various brain abnormalities.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

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.},
 author = {Luo, Guoting and Xie, Wei and Gao, Ronghui and Zheng, Tao and Chen, Lei and Sun, Huaiqiang},
 year = {2023},
 title = {Unsupervised anomaly detection in brain MRI: Learning abstract distribution from massive healthy brains},
 url = {https://www.sciencedirect.com/science/article/pii/S0010482523000756},
 pages = {106610},
 volume = {154},
 issn = {0010-4825},
 journal = {Computers in Biology and Medicine},
 doi = {10.1016/j.compbiomed.2023.106610}
}


@article{Ma.2022,
 abstract = {Outliers in neuroimaging represent spurious data or the data of unusual phenotypes that deserve special attention such as clinical follow-up. Outliers have usually been detected in a supervised or semi-supervised manner for labeled neuroimaging cohorts. There has been much less work using unsupervised outlier detection on large unlabeled cohorts like the UK Biobank brain imaging dataset. Given its large sample size, rare imaging phenotypes within this unique cohort are of interest, as they are often clinically relevant and could be informative for discovering new processes. Here, we developed a two-level outlier detection and screening methodology to characterize individual outliers from the multimodal MRI dataset of more than 15,000 UK Biobank subjects. In primary screening, using brain ventricles, white matter, cortical thickness, and functional connectivity-based imaging phenotypes, every subject was parameterized with an outlier score per imaging phenotype. Outlier scores of these imaging phenotypes had good-to-excellent test-retest reliability, with the exception of resting-state functional connectivity (RSFC). Due to the low reliability of RSFC outlier scores, RSFC outliers were excluded from further individual-level outlier screening. In secondary screening, the extreme outliers (1,026 subjects) were examined individually, and those arising from data collection/processing errors were eliminated. A representative subgroup of 120 subjects from the remaining non-artifactual outliers were radiologically reviewed, and radiological findings were identified in 97.5{\%} of them. This study establishes an unsupervised framework for investigating rare individual imaging phenotypes within a large neuroimaging cohort.},
 author = {Ma, Zhiwei and Reich, Daniel S. and Dembling, Sarah and Duyn, Jeff H. and Koretsky, Alan P.},
 year = {2022},
 title = {Outlier detection in multimodal MRI identifies rare individual phenotypes among more than 15,000 brains},
 url = {https://pubmed.ncbi.nlm.nih.gov/34957633/},
 pages = {1766--1782},
 volume = {43},
 number = {5},
 issn = {1097-0193},
 journal = {Human brain mapping},
 doi = {10.1002/hbm.25756}
}


@article{Maier.2017,
 abstract = {Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).},
 author = {Maier, Oskar and Menze, Bjoern H. and von der Gablentz, Janina and Hani, Levin and Heinrich, Mattias P. and Liebrand, Matthias and Winzeck, Stefan and Basit, Abdul and Bentley, Paul and Chen, Liang and Christiaens, Daan and Dutil, Francis and Egger, Karl and Feng, Chaolu and Glocker, Ben and Goetz, Michael and Haeck, Tom and Halme, Hanna-Leena and Havaei, Mohammad and Iftekharuddin, Khan M. and Jodoin, Pierre-Marc and Kamnitsas, Konstantinos and Kellner, Elias and Korvenoja, Antti and Larochelle, Hugo and Ledig, Christian and Lee, Jia-Hong and Maes, Frederik and Mahmood, Qaiser and Maier-Hein, Klaus H. and McKinley, Richard and Muschelli, John and Pal, Chris and Pei, Linmin and Rangarajan, Janaki Raman and Reza, Syed M. S. and Robben, David and Rueckert, Daniel and Salli, Eero and Suetens, Paul and Wang, Ching-Wei and Wilms, Matthias and Kirschke, Jan S. and {Kr Amer}, Ulrike M. and M{\"u}nte, Thomas F. and Schramm, Peter and Wiest, Roland and Handels, Heinz and Reyes, Mauricio},
 year = {2017},
 title = {ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI},
 pages = {250--269},
 volume = {35},
 issn = {1361-8415},
 journal = {Medical Image Analysis}
}


@article{Makhzani.2013,
 abstract = {Recently, it has been observed that when representations are learnt in a way that encourages sparsity, improved performance is obtained on classification tasks. These methods involve combinations of activation functions, sampling steps and different kinds of penalties. To investigate the effectiveness of sparsity by itself, we propose the k-sparse autoencoder, which is an autoencoder with linear activation function, where in hidden layers only the k highest activities are kept. When applied to the MNIST and NORB datasets, we find that this method achieves better classification results than denoising autoencoders, networks trained with dropout, and RBMs. k-sparse autoencoders are simple to train and the encoding stage is very fast, making them well-suited to large problem sizes, where conventional sparse coding algorithms cannot be applied.},
 author = {Makhzani, Alireza and Frey, Brendan},
 year = {2013},
 title = {k-Sparse Autoencoders},
 url = {https://arxiv.org/pdf/1312.5663},
 journal = {arXiv preprint 1312.5663v2}
}


@proceedings{Martel.2020,
 abstract = {},
 year = {2020},
 title = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-030-59712-2},
 series = {Lecture Notes in Computer Science},
 editor = {Martel, Anne L. and Abolmaesumi, Purang and Stoyanov, Danail and Mateus, Diana and Zuluaga, Maria A. and Zhou, S. Kevin and Racoceanu, Daniel and Joskowicz, Leo}
}


@proceedings{MartinA.Styner.2016,
 abstract = {},
 year = {2016},
 title = {Medical Imaging 2016: Image Processing},
 publisher = {SPIE},
 editor = {{Martin A. Styner} and {Elsa D. Angelini}},
 institution = {{International Society for Optics and Photonics}}
}


@article{Martins.2019,
 abstract = {Most neurological diseases are associated with abnormal brain asymmetries. Recent advances in automatic unsupervised techniques model normal brain asymmetries from healthy subjects only and treat anomalies as outliers. Outlier detection is usually done in a common standard coordinate space that limits its usability. To alleviate the problem, we extend a recent fully unsupervised supervoxel-based approach (SAAD) for abnormal asymmetry detection in the native image space of MR brain images. Experimental results using our new method, called N-SAAD, show that it can achieve higher accuracy in detection with considerably less false positives than a method based on unsupervised deep learning for a large set of MR-T1 images.},
 author = {Martins, Samuel and Telea, Alexandru C. and Falcao, Alexandre X.},
 year = {2019},
 title = {Extending Supervoxel-based Abnormal Brain Asymmetry Detection to the Native Image Space},
 url = {https://pubmed.ncbi.nlm.nih.gov/31945935/},
 pages = {450--453},
 volume = {2019},
 issn = {2694-0604},
 journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
 doi = {10.1109/EMBC.2019.8857447}
}


@inproceedings{Martins.2019b,
 abstract = {Several pathologies are associated with abnormal asymmetries in brain images and their automated detection can improve diagnosis, segmentation, and automatic analysis of abnormal brain tissues (e.g., lesions). In this paper, we introduce a fully unsupervised supervoxel-based approach for abnormal asymmetry detection in MR images of the brain. Also, we present a new method for symmetrical supervoxel extraction called SymmISF. The experiments over a large set of MR-TI images reveal a higher detection rates and considerably less false positives in comparison to a deep learning auto-encoder approach.},
 author = {Martins, Samuel and {G. Ruppert} and {F. Reis} and {C. L. Yasuda} and {A. X. Falc{\~a}o}},
 title = {A Supervoxel-Based Approach for Unsupervised Abnormal Asymmetry Detection in Mr Images of the Brain},
 pages = {882--885},
 isbn = {1945-8452},
 booktitle = {2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
 year = {2019},
 doi = {10.1109/ISBI.2019.8759166}
}


@inproceedings{Martins.2019c,
 abstract = {SPIE Digital Library Proceedings},
 author = {Martins, Samuel and {Barbara Caroline Benato} and {Bruna Ferreira Silva} and {Clarissa Lyn Yasuda} and {Alexandre Xavier Falc{\~a}o}},
 title = {Modeling normal brain asymmetry in MR images applied to anomaly detection without segmentation and data annotation},
 url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10950/2512873/Modeling-normal-brain-asymmetry-in-MR-images-applied-to-anomaly/10.1117/12.2512873.full},
 pages = {71--80},
 publisher = {SPIE},
 year = {2019},
 doi = {10.1117/12.2512873}
}


@article{Martins.2020,
 abstract = {Several brain disorders are associated with abnormal brain asymmetries (asymmetric anomalies). Several computer-based methods aim to detect such anomalies automatically. Recent advances in this area use automatic unsupervised techniques that extract pairs of symmetric supervoxels in the hemispheres, model normal brain asymmetries for each pair from healthy subjects, and treat outliers as anomalies. Yet, there is no deep understanding of the impact of the supervoxel segmentation quality for abnormal asymmetry detection, especially for small anomalies, nor of the added value of using a specialized model for each supervoxel pair instead of a single global appearance model. We aim to answer these questions by a detailed evaluation of different scenarios for supervoxel segmentation and classification for detecting abnormal brain asymmetries. Experimental results on 3D MR-T1 brain images of stroke patients confirm the importance of high-quality supervoxels fit anomalies and the use of a specific classifier for each supervoxel. Next, we present a refinement of the detection method that reduces the number of false-positive supervoxels, thereby making the detection method easier to use for visual inspection and analysis of the found anomalies.},
 author = {Martins, Samuel and Telea, Alexandru C. and Falc{\~a}o, Alexandre X.},
 year = {2020},
 title = {Investigating the impact of supervoxel segmentation for unsupervised abnormal brain asymmetry detection},
 url = {https://www.sciencedirect.com/science/article/pii/S0895611120300720},
 pages = {101770},
 volume = {85},
 journal = {Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society},
 doi = {10.1016/j.compmedimag.2020.101770}
}


@article{MatthausHeer.2021,
 abstract = {The OOD Blind Spot of Unsupervised Anomaly DetectionMatth{\"a}us Heer,~Janis Postels,~Xiaoran Chen,~Ender Konukoglu,~Shadi AlbarqouniDeep uns...},
 author = {Heer, Matth{\"a}us  and Postels, Janis and Chen, Xiaoran and Konukoglu, Ender and Albarqouni, Shadi },
 year = {2021},
 title = {The OOD Blind Spot of Unsupervised Anomaly Detection},
 url = {https://proceedings.mlr.press/v143/heer21a.html},
 keywords = {Meta Information},
 pages = {286--300},
 issn = {2640-3498},
 journal = {Medical Imaging with Deep Learning}
}


@article{Mazziotta.2001,
 abstract = {Motivated by the vast amount of information that is rapidly accumulating about the human brain in digital form, we embarked upon a program in 1992 to develop a four-dimensional probabilistic atlas and reference system for the human brain. Through an International Consortium for Brain Mapping (ICBM) a dataset is being collected that includes 7000 subjects between the ages of eighteen and ninety years and including 342 mono- and dizygotic twins. Data on each subject includes detailed demographic, clinical, behavioural and imaging information. DNA has been collected for genotyping from 5800 subjects. A component of the programme uses post-mortem tissue to determine the probabilistic distribution of microscopic cyto- and chemoarchitectural regions in the human brain. This, combined with macroscopic information about structure and function derived from subjects in vivo, provides the first large scale opportunity to gain meaningful insights into the concordance or discordance in micro- and macroscopic structure and function. The philosophy, strategy, algorithm development, data acquisition techniques and validation methods are described in this report along with database structures. Examples of results are described for the normal adult human brain as well as examples in patients with Alzheimer's disease and multiple sclerosis. The ability to quantify the variance of the human brain as a function of age in a large population of subjects for whom data is also available about their genetic composition and behaviour will allow for the first assessment of cerebral genotype-phenotype-behavioural correlations in humans to take place in a population this large. This approach and its application should provide new insights and opportunities for investigators interested in basic neuroscience, clinical diagnostics and the evaluation of neuropsychiatric disorders in patients.},
 author = {Mazziotta, J. and Toga, A. and Evans, A. and Fox, P. and Lancaster, J. and Zilles, K. and Woods, R. and Paus, T. and Simpson, G. and Pike, B. and Holmes, C. and Collins, L. and Thompson, P. and MacDonald, D. and Iacoboni, M. and Schormann, T. and Amunts, K. and Palomero-Gallagher, N. and Geyer, S. and Parsons, L. and Narr, K. and Kabani, N. and {Le Goualher}, G. and Boomsma, D. and Cannon, T. and Kawashima, R. and Mazoyer, B.},
 year = {2001},
 title = {A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM)},
 keywords = {Adult;Brain Mapping/instrumentation/methods;Brain/anatomy {\&} histology;Databases, Factual;Humans;Magnetic Resonance Imaging;Models, Statistical;Neuroanatomy/instrumentation/methods},
 pages = {1293--1322},
 volume = {356},
 number = {1412},
 issn = {0962-8436},
 journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
 doi = {10.1098/rstb.2001.0915}
}


@article{McDonald.2015,
 abstract = {RATIONALE AND OBJECTIVES

To examine the effect of changes in utilization and advances in cross-sectional imaging on radiologists' workload.

MATERIALS AND METHODS

All computed tomography (CT) and magnetic resonance imaging (MRI) examinations performed at a single institution between 1999 and 2010 were identified and associated with the total number of images for each examination. Annual trends in institutional numbers of interpreted examinations and images were translated to changes in daily workload for the individual radiologist by normalizing to the number of dedicated daily CT and MRI work assignments, assuming a 255-day/8-hour work day schedule. Temporal changes in institutional and individual workload were assessed by Sen's slope analysis (Q = median slope) and Mann-Kendall test (Z = Z statistic).

RESULTS

From 1999 to 2010, a total of 1,517,149 cross-sectional imaging studies (CT = 994,471; MRI = 522,678) comprising 539,210,581 images (CT = 339,830,947; MRI = 199,379,634) were evaluated at our institution. Total annual cross-sectional studies steadily increased from 84,409 in 1999 to 147,336 in 2010, representing a twofold increase in workload (Q = 6465/year, Z = 4.2, P {\textless} .0001). Concomitantly, the number of annual departmental cross-sectional images interpreted increased from 9,294,140 in 1990 to 94,271,551 in 2010, representing a 10-fold increase (Q = 8707876/year, Z = 4.5, P {\textless} .0001). Adjusting for staffing changes, the number of images requiring interpretation per minute of every workday per staff radiologist increased from 2.9 in 1999 to 16.1 in 2010 (Q = 1.7/year, Z = 4.3, P {\textless} .0001).

CONCLUSIONS

Imaging volumes have grown at a disproportionate rate to imaging utilization increases at our institution. The average radiologist interpreting CT or MRI examinations must now interpret one image every 3-4 seconds in an 8-hour workday to meet workload demands.},
 author = {McDonald, Robert J. and Schwartz, Kara M. and Eckel, Laurence J. and Diehn, Felix E. and Hunt, Christopher H. and Bartholmai, Brian J. and Erickson, Bradley J. and Kallmes, David F.},
 year = {2015},
 title = {The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload},
 pages = {1191--1198},
 volume = {22},
 number = {9},
 journal = {Academic radiology},
 doi = {10.1016/j.acra.2015.05.007}
}


@misc{Meissen.2022c,
 abstract = {Many current state-of-the-art methods for anomaly localization in medical images rely on calculating a residual image between a potentially anomalous input image and its {\textquotedbl}healthy{\textquotedbl} reconstruction. As the reconstruction of the unseen anomalous region should be erroneous, this yields large residuals as a score to detect anomalies in medical images. However, this assumption does not take into account residuals resulting from imperfect reconstructions of the machine learning models used. Such errors can easily overshadow residuals of interest and therefore strongly question the use of residual images as scoring function. Our work explores this fundamental problem of residual images in detail. We theoretically define the problem and thoroughly evaluate the influence of intensity and texture of anomalies against the effect of imperfect reconstructions in a series of experiments. Code and experiments are available under https://github.com/FeliMe/residual-score-pitfalls},
 author = {Meissen, Felix and Wiestler, Benedikt and Kaissis, Georgios and Rueckert, Daniel},
 year = {2022},
 title = {On the Pitfalls of Using the Residual Error as Anomaly Score},
 url = {https://arxiv.org/pdf/2202.03826},
 keywords = {Meta Information}
}


@inproceedings{Meissen.2022,
 abstract = {In medical imaging, un-, semi-, or self-supervised pathology detection is often approached with anomaly- or out-of-distribution detection methods, whose inductive biases are not intentionally directed towards detecting pathologies, and are therefore sub-optimal for...},
 author = {Meissen, Felix and Kaissis, Georgios and Rueckert, Daniel},
 title = {AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation},
 url = {https://link.springer.com/chapter/10.1007/978-3-030-97281-3_19},
 pages = {127--135},
 publisher = {{Springer, Cham}},
 year = {2022},
 doi = {10.1007/978-3-030-97281-3{\textunderscore }19}
}


@inproceedings{Meissen.2022b,
 abstract = {In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of automatically identifying pathologies in brain images. Our work challenges the effectiveness of current Machine Learning (ML) approaches in this application domain by showing that thresholding Fluid-attenuated inversion recovery (FLAIR) MR scans provides better anomaly segmentation maps than several different ML-based anomaly detection models. Specifically, our method achieves better Dice similarity coefficients and Precision-Recall curves than the competitors on various popular evaluation data sets for the segmentation of tumors and multiple sclerosis lesions. (Code available under: https://github.com/FeliMe/brain{\_}sas{\_}baseline)},
 author = {Meissen, Felix and Kaissis, Georgios and Rueckert, Daniel},
 title = {Challenging Current Semi-supervised Anomaly Segmentation Methods for~Brain MRI},
 pages = {63--74},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-031-08999-2},
 editor = {{Crimi, Alessandro and Bakas, Spyridon}},
 booktitle = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries},
 year = {2022},
 address = {Cham}
}


@article{Menze.2014,
 abstract = {},
 author = {Menze, Bjoern H. and Jakab, Andras and Bauer, Stefan and Kalpathy-Cramer, Jayashree and Farahani, Keyvan and Kirby, Justin and Burren, Yuliya and Porz, Nicole and Slotboom, Johannes and Wiest, Roland and others},
 year = {2014},
 title = {The multimodal brain tumor image segmentation benchmark (BRATS)},
 pages = {1993--2024},
 volume = {34},
 number = {10},
 issn = {0278-0062},
 journal = {IEEE transactions on medical imaging}
}


@article{Minaee.2018,
 abstract = {Mild traumatic brain injury is a growing public health problem with an estimated incidence of over 1.7 million people annually in US. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. This work aims to directly use diffusion MR images obtained within one month of trauma to detect injury, by incorporating deep learning techniques. To overcome the challenge due to limited training data, we describe each brain region using the bag of word representation, which specifies the distribution of representative patch patterns. We apply a convolutional auto-encoder to learn the patch-level features, from overlapping image patches extracted from the MR images, to learn features from diffusion MR images of brain using an unsupervised approach. Our experimental results show that the bag of word representation using patch level features learnt by the auto encoder provides similar performance as that using the raw patch patterns, both significantly outperform earlier work relying on the mean values of MR metrics in selected brain regions.},
 author = {Minaee, Shervin and Wang, Yao and Choromanska, Anna and Chung, Sohae and Wang, Xiuyuan and Fieremans, Els and Flanagan, Steven and Rath, Joseph and Lui, Yvonne W.},
 year = {2018},
 title = {A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI},
 pages = {1267--1270},
 volume = {2018},
 issn = {2694-0604},
 journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
 doi = {10.1109/EMBC.2018.8512556}
}


@inproceedings{Mostapha.2019,
 abstract = {Discriminative deep learning models have shown remarkable success in many medical image analysis applications. However, their success is limited in problems that involve learning from imbalanced and heterogeneous datasets. Generative models parameterized using deep...},
 author = {Mostapha, Mahmoud and Prieto, Juan and Murphy, Veronica and Girault, Jessica and Foster, Mark and Rumple, Ashley and Blocher, Joseph and Lin, Weili and Elison, Jed and Gilmore, John and Pizer, Steve and Styner, Martin},
 title = {Semi-supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control},
 url = {https://link.springer.com/chapter/10.1007/978-3-030-32248-9_15},
 pages = {127--136},
 publisher = {{Springer, Cham}},
 year = {2019},
 doi = {10.1007/978-3-030-32248-9{\textunderscore }15}
}


@article{Mukherjee.2019,
 abstract = {White matter injury (WMI) is the most prevalent brain injury in the preterm neonate leading to developmental deficits. However, detecting WMI in magnetic resonance (MR) images of preterm neonate brains using traditional WM segmentation-based methods is difficult mainly due to lack of reliable preterm neonate brain atlases to guide segmentation. Hence, we propose a segmentation-free, fast, unsupervised, atlas-free WMI detection method. We detect the ventricles as blobs using a fast linear maximally stable extremal regions algorithm. A reference contour equidistant from the blobs and the brain-background boundary is used to identify tissue adjacent to the blobs. Assuming normal distribution of the gray-value intensity of this tissue, the outlier intensities in the entire brain region are identified as potential WMI candidates. Thereafter, false positives are discriminated using appropriate heuristics. Experiments using an expert-annotated dataset show that the proposed method runs 20 times faster than our earlier work which relied on time-consuming segmentation of the WM region, without compromising WMI detection accuracy. Graphical Abstract Key Steps of Segmentation-free WMI Detection.},
 author = {Mukherjee, Subhayan and Cheng, Irene and Miller, Steven and Guo, Ting and Chau, Vann and Basu, Anup},
 year = {2019},
 title = {A fast segmentation-free fully automated approach to white matter injury detection in preterm infants},
 url = {https://pubmed.ncbi.nlm.nih.gov/29981051/},
 pages = {71--87},
 volume = {57},
 number = {1},
 issn = {1741-0444},
 journal = {Medical {\&} biological engineering {\&} computing},
 doi = {10.1007/s11517-018-1829-9}
}


@article{MunozRamirez.2022,
 abstract = {With the advent of recent deep learning techniques, computerized methods for automatic lesion segmentation have reached performances comparable to those of medical practitioners. However, little attention has been paid to the detection of subtle physiological changes caused by evolutive pathologies, such as neurodegenerative diseases. In this work, we leverage deep learning models to detect anomalies in brain diffusion tensor imaging (DTI) parameter maps of recently diagnosed and untreated (de novo) patients with Parkinson's disease (PD). For this purpose, we trained auto-encoders on parameter maps of healthy controls (n~=~56) and tested them on those of de novo PD patients (n~=~129). We considered large reconstruction errors between the original and reconstructed images to be anomalies that, when quantified, allow discerning between de novo PD patients and healthy controls. The most discriminating brain macro-region was found to be the white matter with a ROC-AUC 68.3 (IQR 5.4) and the best subcortical structure, the GPi (ROC-AUC 62.6 IQR 5.4). Our results indicate that our deep learning-based model can detect potentially pathological regions in de novo PD patients, without requiring any expert delineation. This may enable extracting neuroimaging biomarkers of PD in the future, but further testing on larger cohorts is needed. Such models can be seamlessly extended with additional parameter maps and applied to study the physio-pathology of other neurological diseases.},
 author = {Mu{\~n}oz-Ram{\'i}rez, Ver{\'o}nica and Kmetzsch, Virgilio and Forbes, Florence and Meoni, Sara and Moro, Elena and Dojat, Michel},
 year = {2022},
 title = {Subtle anomaly detection: Application to brain MRI analysis of de novo Parkinsonian patients},
 url = {https://www.sciencedirect.com/science/article/pii/S0933365722000161},
 pages = {102251},
 volume = {125},
 issn = {0933-3657},
 journal = {Artificial Intelligence in Medicine},
 doi = {10.1016/j.artmed.2022.102251}
}


@inproceedings{Myronenko.2019,
 abstract = {},
 author = {Myronenko, Andriy},
 title = {3D MRI Brain Tumor Segmentation Using Autoencoder Regularization},
 pages = {311--320},
 volume = {11384},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-030-11725-2},
 series = {Lecture Notes in Computer Science},
 editor = {Crimi, Alessandro and Bakas, Spyridon and Kuijf, Hugo and Keyvan, Farahani and Reyes, Mauricio and {van Walsum}, Theo},
 booktitle = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries},
 year = {2019},
 address = {Cham},
 doi = {10.1007/978-3-030-11726-9{\textunderscore }28}
}


@inproceedings{Marimont.2021,
 abstract = {We propose a novel unsupervised out-of-distribution detection method for medical images based on implicit fields image representations. In our approach, an auto-decoder feed-forward neural network learns the distribution of healthy images in the form of a mapping...},
 author = {{Naval Marimont}, Sergio and Tarroni, Giacomo},
 title = {Implicit Field Learning for Unsupervised Anomaly Detection in Medical Images},
 url = {https://link.springer.com/chapter/10.1007/978-3-030-87196-3_18},
 pages = {189--198},
 publisher = {{Springer, Cham}},
 year = {2021},
 doi = {10.1007/978-3-030-87196-3{\textunderscore }18}
}
@inproceedings{Marimont.2021b,
  author={Marimont, Sergio Naval and Tarroni, Giacomo},
  booktitle={2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)}, 
  title={Anomaly Detection Through Latent Space Restoration Using Vector Quantized Variational Autoencoders}, 
  year={2021},
  volume={},
  number={},
  pages={1764-1767},
  doi={10.1109/ISBI48211.2021.9433778}}

@inproceedings{Nguyen.2021,
 abstract = {},
 author = {Nguyen, Bao and Feldman, Adam and Bethapudi, Sarath and Jennings, Andrew and Willcocks, Chris G.},
 title = {Unsupervised Region-Based Anomaly Detection In Brain MRI With Adversarial Image Inpainting},
 pages = {1127--1131},
 booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
 year = {2021},
 doi = {10.1109/ISBI48211.2021.9434115}
}


@article{NicolasPinon.2023,
 abstract = {We propose a new Unsupervised Anomaly Detection method, evaluate it on the WMH challenge database and compare it to the best SOTA methods.},
 author = {Pinon, Nicolas  and Trombetta, Robin and Lartizien, Carole},
 year = {2023},
 title = {One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities},
 url = {https://openreview.net/forum?id=_c9r6-HCEaN},
 issn = {2640-3498},
 journal = {Medical Imaging with Deep Learning}
}


@inproceedings{Park.2022,
 abstract = {Mitigating out-of-distribution inaccuracies due to insufficient labeled data is a widespread problem in the medical AI field. To tackle this medical out-of-distribution problem, through the medical out-of-distribution challenge [1], our team utilized self-supervised...},
 author = {Park, Seongjin and Balint, Adam and Hwang, Hyejin},
 title = {Self-supervised Medical Out-of-Distribution Using U-Net Vision Transformers},
 url = {https://link.springer.com/chapter/10.1007/978-3-030-97281-3_16},
 pages = {104--110},
 publisher = {{Springer, Cham}},
 year = {2022},
 doi = {10.1007/978-3-030-97281-3{\textunderscore }16}
}


@article{Pathak.2016,
 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.},
 author = {Pathak, Deepak and Krahenbuhl, Philipp and Donahue, Jeff and Darrell, Trevor and Efros, Alexei A.},
 year = {2016},
 title = {Context Encoders: Feature Learning by Inpainting},
 url = {https://arxiv.org/pdf/1604.07379},
 journal = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}
}


@article{Pernet.2016,
 abstract = {We collected high resolution structural (T1, T2, DWI) and several functional (BOLD T2*) MRI data in 22 patients with different types of brain tumours. Functional imaging protocols included a motor task, a verb generation task, a word repetition task and resting state. Imaging data are complemented by demographics (age, sex, handedness, and pathology), behavioural results to motor and cognitive tests and direct cortical electrical stimulation data (pictures of stimulation sites with outcomes) performed during surgery. Altogether, these data are suited to test functional imaging methods for single subject analyses, in particular methods that focus on locating eloquent cortical areas, critical functional and/or structural network hubs, and predict patient status based on imaging data (presurgical mapping).},
 author = {Pernet, Cyril R. and Gorgolewski, Krzysztof J. and Job, Dominic and Rodriguez, David and Whittle, Ian and Wardlaw, Joanna},
 year = {2016},
 title = {A structural and functional magnetic resonance imaging dataset of brain tumour patients},
 keywords = {Brain Mapping;Brain Neoplasms/diagnosis/pathology/physiopathology/surgery;Cognition;Electric Stimulation;Humans;Magnetic Resonance Imaging;Motor Activity;Neuronavigation},
 pages = {160003},
 volume = {3},
 journal = {Scientific data},
 doi = {10.1038/sdata.2016.3}
}


@proceedings{Petrick.2018,
 abstract = {},
 year = {2018},
 title = {Medical Imaging 2018: Computer-Aided Diagnosis: 12-15 February 2018, Houston, Texas, United States},
 address = {Bellingham, Washington, USA},
 volume = {vol. 19, no. 47},
 publisher = {SPIE},
 isbn = {9781510616394},
 series = {Progress in biomedical optics and imaging},
 editor = {Petrick, Nicholas and Mori, Kensaku},
 institution = {{Computer-Aided Diagnosis} and SPIE and {DECTRIS Ltd} and {American Association of Physicists in Medicine} and {SPIE Medical Imaging} and {Medical Imaging} and {SPIE Medical Imaging Conference}}
}


@inproceedings{Pinaya.2021,
 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.},
 author = {Pinaya, Walter Hugo Lopez and Tudosiu, Petru-Daniel and Gray, Robert and Rees, Geraint and Nachev, Parashkev and Ourselin, S{\'e}bastien and Cardoso, M. Jorge},
 title = {Unsupervised Brain Anomaly Detection and Segmentation with Transformers},
 url = {https://proceedings.mlr.press/v143/pinaya21a.html},
 pages = {596--617},
 volume = {143},
 publisher = {PMLR},
 series = {Proceedings of Machine Learning Research},
 editor = {Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schl{\"a}fer, Alexander and Ernst, Floris},
 booktitle = {Proceedings of the Fourth Conference on Medical Imaging with Deep Learning},
 year = {2021}
}


@article{Pinaya.2022,
 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 set of pathological features. 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 resources. 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 with 2D and 3D data 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/voxel-wise, achievable without post-processing. These results draw attention to the potential of transformers in this most challenging of imaging tasks.},
 author = {Pinaya, Walter H. L. and Tudosiu, Petru-Daniel and Gray, Robert and Rees, Geraint and Nachev, Parashkev and Ourselin, Sebastien and Cardoso, M. Jorge},
 year = {2022},
 title = {Unsupervised brain imaging 3D anomaly detection and segmentation with transformers},
 keywords = {Brain Diseases;Brain/diagnostic imaging;Humans;Neuroimaging},
 pages = {102475},
 volume = {79},
 issn = {1361-8415},
 journal = {Medical Image Analysis},
 doi = {10.1016/j.media.2022.102475}
}


@inproceedings{Pinaya.2022b,
 abstract = {},
 author = {Pinaya, Walter H. L. and Graham, Mark S. and Gray, Robert and {Da Costa}, Pedro F. and Tudosiu, Petru-Daniel and Wright, Paul and Mah, Yee H. and MacKinnon, Andrew D. and Teo, James T. and Jager, Rolf and others},
 title = {Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models},
 pages = {705--714},
 booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
 year = {2022}
}


@article{Pinho.2020,
 abstract = {We present an extension of the Individual Brain Charting dataset -a high spatial-resolution, multi-task, functional Magnetic Resonance Imaging dataset, intended to support the investigation on the functional principles governing cognition in the human brain. The concomitant data acquisition from the same 12 participants, in the same environment, allows to obtain in the long run finer cognitive topographies, free from inter-subject and inter-site variability. This second release provides more data from psychological domains present in the first release, and also yields data featuring new ones. It includes tasks on e.g. mental time travel, reward, theory-of-mind, pain, numerosity, self-reference effect and speech recognition. In total, 13 tasks with 86 contrasts were added to the dataset and 63 new components were included in the cognitive description of the ensuing contrasts. As the dataset becomes larger, the collection of the corresponding topographies becomes more comprehensive, leading to better brain-atlasing frameworks. This dataset is an open-access facility; raw data and derivatives are publicly available in neuroimaging repositories.},
 author = {Pinho, Ana Lu{\'i}sa and Amadon, Alexis and Gauthier, Baptiste and Clairis, Nicolas and Knops, Andr{\'e} and Genon, Sarah and Dohmatob, Elvis and Torre, Juan Jes{\'u}s and Ginisty, Chantal and Becuwe-Desmidt, S{\'e}verine and Roger, S{\'e}verine and Lecomte, Yann and Berland, Val{\'e}rie and Laurier, Laurence and Joly-Testault, V{\'e}ronique and M{\'e}diouni-Cloarec, Ga{\"e}lle and Doubl{\'e}, Christine and Martins, Bernadette and Salmon, Eric and Piazza, Manuela and Melcher, David and Pessiglione, Mathias and {van Wassenhove}, Virginie and Eger, Evelyn and Varoquaux, Ga{\"e}l and Dehaene, Stanislas and Hertz-Pannier, Lucie and Thirion, Bertrand},
 year = {2020},
 title = {Individual Brain Charting dataset extension, second release of high-resolution fMRI data for cognitive mapping},
 pages = {353},
 volume = {7},
 number = {1},
 journal = {Scientific data},
 doi = {10.1038/s41597-020-00670-4}
}


@inproceedings{Pinon.2023,
 abstract = {},
 author = {Pinon, Nicolas and Oudoumanessah, Geoffroy and Trombetta, Robin and Dojat, Michel and Forbes, Florence and Lartizien, Carole},
 title = {Brain Subtle Anomaly Detection Based on Auto-Encoders Latent Space Analysis: Application To De Novo Parkinson Patients},
 pages = {1--5},
 booktitle = {2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)},
 year = {2023},
 doi = {10.1109/ISBI53787.2023.10230351}
}


@article{Porz.2014,
 abstract = {BACKGROUND AND PURPOSE

Reproducible segmentation of brain tumors on magnetic resonance images is an important clinical need. This study was designed to evaluate the reliability of a novel fully automated segmentation tool for brain tumor image analysis in comparison to manually defined tumor segmentations.

METHODS

We prospectively evaluated preoperative MR Images from 25 glioblastoma patients. Two independent expert raters performed manual segmentations. Automatic segmentations were performed using the Brain Tumor Image Analysis software (BraTumIA). In order to study the different tumor compartments, the complete tumor volume TV (enhancing part plus non-enhancing part plus necrotic core of the tumor), the TV+ (TV plus edema) and the contrast enhancing tumor volume CETV were identified. We quantified the overlap between manual and automated segmentation by calculation of diameter measurements as well as the Dice coefficients, the positive predictive values, sensitivity, relative volume error and absolute volume error.

RESULTS

Comparison of automated versus manual extraction of 2-dimensional diameter measurements showed no significant difference (p = 0.29). Comparison of automated versus manual segmentation of volumetric segmentations showed significant differences for TV+ and TV (p{\textless}0.05) but no significant differences for CETV (p{\textgreater}0.05) with regard to the Dice overlap coefficients. Spearman's rank correlation coefficients (\textgreek{r}) of TV+, TV and CETV showed highly significant correlations between automatic and manual segmentations. Tumor localization did not influence the accuracy of segmentation.

CONCLUSIONS

In summary, we demonstrated that BraTumIA supports radiologists and clinicians by providing accurate measures of cross-sectional diameter-based tumor extensions. The automated volume measurements were comparable to manual tumor delineation for CETV tumor volumes, and outperformed inter-rater variability for overlap and sensitivity.},
 author = {Porz, Nicole and Bauer, Stefan and Pica, Alessia and Schucht, Philippe and Beck, J{\"u}rgen and Verma, Rajeev Kumar and Slotboom, Johannes and Reyes, Mauricio and Wiest, Roland},
 year = {2014},
 title = {Multi-modal glioblastoma segmentation: man versus machine},
 keywords = {Aged;Brain Neoplasms/diagnostic imaging/pathology;Contrast Media;Female;Glioblastoma/diagnostic imaging/pathology;Humans;Image Interpretation, Computer-Assisted;Magnetic Resonance Imaging/methods;Male;Middle Aged;Pattern Recognition, Automated;Radiography;Software;Tumor Burden},
 pages = {e96873},
 volume = {9},
 number = {5},
 journal = {PloS one},
 doi = {10.1371/journal.pone.0096873}
}


@article{PPMI,
 abstract = {The Parkinson Progression Marker Initiative (PPMI) is a comprehensive observational, international, multi-center study designed to identify PD progression biomarkers both to improve understanding of disease etiology and course and to provide crucial tools to enhance the likelihood of success of PD modifying therapeutic trials. The PPMI cohort will comprise 400 recently diagnosed PD and 200 healthy subjects followed longitudinally for clinical, imaging and biospecimen biomarker assessment using standardized data acquisition protocols at twenty-one clinical sites. All study data will be integrated in the PPMI study database and will be rapidly and publically available through the PPMI web site- www.ppmi-info.org. Biological samples including longitudinal collection of blood, cerebrospinal fluid (CSF) and urine will be available to scientists by application to an independent PPMI biospecimen review committee also through the PPMI web site. PPMI will rely on a partnership of government, PD foundations, industry and academics working cooperatively. This approach is crucial to enhance the potential for success of this ambitious strategy to develop PD progression biomarkers that will accelerate research in disease modifying therapeutics.},
 year = {2011},
 title = {The Parkinson Progression Marker Initiative (PPMI)},
 keywords = {Biomarkers/metabolism;Cohort Studies;Diagnostic Imaging;Disease Progression;Female;Humans;International Cooperation;Male;Multicenter Studies as Topic/methods;Parkinson Disease/blood/cerebrospinal fluid/diagnosis},
 pages = {629--635},
 volume = {95},
 number = {4},
 journal = {Progress in neurobiology},
 doi = {10.1016/j.pneurobio.2011.09.005}
}


@article{Puccio.2016,
 abstract = {BACKGROUND

Skull-stripping is the procedure of removing non-brain tissue from anatomical MRI data. This procedure can be useful for calculating brain volume and for improving the quality of other image processing steps. Developing new skull-stripping algorithms and evaluating their performance requires gold standard data from a variety of different scanners and acquisition methods. We complement existing repositories with manually corrected brain masks for 125 T1-weighted anatomical scans from the Nathan Kline Institute Enhanced Rockland Sample Neurofeedback Study.

FINDINGS

Skull-stripped images were obtained using a semi-automated procedure that involved skull-stripping the data using the brain extraction based on nonlocal segmentation technique (BEaST) software, and manually correcting the worst results. Corrected brain masks were added into the BEaST library and the procedure was repeated until acceptable brain masks were available for all images. In total, 85 of the skull-stripped images were hand-edited and 40 were deemed to not need editing. The results are brain masks for the 125 images along with a BEaST library for automatically skull-stripping other data.

CONCLUSION

Skull-stripped anatomical images from the Neurofeedback sample are available for download from the Preprocessed Connectomes Project. The resulting brain masks can be used by researchers to improve preprocessing of the Neurofeedback data, as training and testing data for developing new skull-stripping algorithms, and for evaluating the impact on other aspects of MRI preprocessing. We have illustrated the utility of these data as a reference for comparing various automatic methods and evaluated the performance of the newly created library on independent data.},
 author = {Puccio, Benjamin and Pooley, James P. and Pellman, John S. and Taverna, Elise C. and Craddock, R. Cameron},
 year = {2016},
 title = {The preprocessed connectomes project repository of manually corrected skull-stripped T1-weighted anatomical MRI data},
 keywords = {Adult;Algorithms;Brain/anatomy {\&} histology;Connectome/methods;Female;Humans;Image Processing, Computer-Assisted/methods;Imaging, Three-Dimensional/methods;Magnetic Resonance Imaging/methods;Male;Skull/anatomy {\&} histology;Software;Young Adult},
 pages = {45},
 volume = {5},
 number = {1},
 journal = {GigaScience},
 doi = {10.1186/s13742-016-0150-5}
}


@article{Raad.2023,
 abstract = {Analysis of 3D medical imaging data has been a large topic of focus in the area of Machine Learning/Artificial Intelligence, though little work has been done in algorithmic (particularly unsupervised) analysis of neonatal brain MRI's. A myriad of conditions can manifest at an early age, including neonatal encephalopathy (NE), which can result in lifelong physical consequences. As such, there is a dire need for better biomarkers of NE and other conditions. The objective of the study is to improve identification of anomalies and prognostication of neonatal MRI brain scans. We introduce a framework designed to support the analysis and assessment of neonatal MRI brain scans, the results of which can be used as an aid to neuroradiologists. We explored the efficacy of the framework through iterations of several deep convolutional Autoencoder (AE) unsupervised modeling architectures designed to learn normalcy of the neonatal brain structure. We tested this framework on the developing human connectome project (dHCP) dataset with 97 patients that were previously categorized by severity. Our framework demonstrated the model's ability to identify and distinguish subtle morphological signatures present in brain structures. Normal and abnormal neonatal brain scans can be distinguished with reasonable accuracy, correctly categorizing them in up to 83{\%} of cases. Most critically, new brain anomalies originally missed during the radiological reading were identified and corroborated by a neuroradiologist. This framework and our modeling approach demonstrate an ability to improve prognostication of neonatal brain conditions and are able to localize new anomalies.},
 author = {Raad, Jad Dino and Chinnam, Ratna Babu and Arslanturk, Suzan and Tan, Sidhartha and Jeong, Jeong-Won and Mody, Swati},
 year = {2023},
 title = {Unsupervised abnormality detection in neonatal MRI brain scans using deep learning},
 keywords = {Artificial Intelligence;Brain Diseases/diagnostic imaging;Brain/diagnostic imaging;Deep Learning;Humans;Image Processing, Computer-Assisted/methods;Infant, Newborn;Magnetic Resonance Imaging/methods},
 pages = {11489},
 volume = {13},
 number = {1},
 journal = {Scientific reports},
 doi = {10.1038/s41598-023-38430-0}
}


@inproceedings{Rifai.2011,
 abstract = {We present in this paper a novel approach for training deterministic auto-encoders. We show that by adding a well chosen penalty term to the classical reconstruction cost function, we can achieve results that equal or surpass those attained by other regularized auto-encoders as well as denoising auto-encoders on a range of datasets. This penalty term corresponds to the Frobenius norm of the Jacobian matrix of the encoder activations with respect to the input. We show that this penalty term results in a localized space contraction which in turn yields robust features on the activation layer. Furthermore, we show how this penalty term is related to both regularized auto-encoders and denoising auto-encoders and how it can be seen as a link between deterministic and non-deterministic auto-encoders. We find empirically that this penalty helps to carve a representation that better captures the local directions of variation dictated by the data, corresponding to a lower-dimensional non-linear manifold, while being more invariant to the vast majority of directions orthogonal to the manifold. Finally, we show that by using the learned features to initialize a MLP, we achieve state of the art classification error on a range of datasets, surpassing other methods of pretraining.},
 author = {Rifai, Salah and Vincent, Pascal and Muller, Xavier and Glorot, Xavier and Bengio, Yoshua},
 title = {Contractive Auto-Encoders: Explicit Invariance During Feature Extraction},
 url = {https://openreview.net/forum?id=HkZN5j-dZH},
 pages = {833--840},
 publisher = {Omnipress},
 series = {ICML'11},
 booktitle = {(Proceedings of the 28th International Conference on International Conference on Machine Learning},
 year = {2011},
 address = {Bellevue, Washington, USA}
}


@article{Rohlfing.2010,
 abstract = {This article describes the SRI24 atlas, a new standard reference system of normal human brain anatomy, that was created using template-free population registration of high-resolution magnetic resonance images acquired at 3T in a group of 24 normal control subjects. The atlas comprises anatomical channels (T1, T2, and proton density weighted), diffusion-related channels (fractional anisotropy, mean diffusivity, longitudinal diffusivity, mean diffusion-weighted image), tissue channels (CSF probability, gray matter probability, white matter probability, tissue labels), and two cortical parcellation maps. The SRI24 atlas enables multichannel atlas-to-subject image registration. It is uniquely versatile in that it is equally suited for the two fundamentally different atlas applications: label propagation and spatial normalization. Label propagation, herein demonstrated using diffusion tensor image fiber tracking, is enabled by the increased sharpness of the SRI24 atlas compared with other available atlases. Spatial normalization, herein demonstrated using data from a young-old group comparison study, is enabled by its unbiased average population shape property. For both propagation and normalization, we also report the results of quantitative comparisons with seven other published atlases: Colin27, MNI152, ICBM452 (warp5 and air12), and LPBA40 (SPM5, FLIRT, AIR). Our results suggest that the SRI24 atlas, although based on 3T MR data, allows equally accurate spatial normalization of data acquired at 1.5T as the comparison atlases, all of which are based on 1.5T data. Furthermore, the SRI24 atlas is as suitable for label propagation as the comparison atlases and detailed enough to allow delineation of anatomical structures for this purpose directly in the atlas.},
 author = {Rohlfing, Torsten and Zahr, Natalie M. and Sullivan, Edith V. and Pfefferbaum, Adolf},
 year = {2010},
 title = {The SRI24 multichannel atlas of normal adult human brain structure},
 keywords = {Access to Information;Adult;Aged;Aged, 80 and over;Aging/pathology;Algorithms;Atlases as Topic;Brain/anatomy {\&} histology/pathology;Diffusion Magnetic Resonance Imaging/methods;Female;Humans;Image Processing, Computer-Assisted;Imaging, Three-Dimensional;Internet;Magnetic Resonance Imaging/methods;Male;Neural Pathways/anatomy {\&} histology/pathology;Probability;Young Adult},
 pages = {798--819},
 volume = {31},
 number = {5},
 issn = {1097-0193},
 journal = {Human brain mapping},
 doi = {10.1002/hbm.20906}
}


@inproceedings{Ronneberger.2015,
 abstract = {},
 author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
 title = {U-net: Convolutional networks for biomedical image segmentation},
 pages = {234--241},
 booktitle = {Medical Image Computing and Computer Assisted Intervention},
 year = {2015}
}


@article{Roura.2015,
 abstract = {INTRODUCTION

Lesion segmentation plays an important role in the diagnosis and follow-up of multiple sclerosis (MS). This task is very time-consuming and subject to intra- and inter-rater variability. In this paper, we present a new tool for automated MS lesion segmentation using T1w and fluid-attenuated inversion recovery (FLAIR) images.

METHODS

Our approach is based on two main steps, initial brain tissue segmentation according to the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) performed in T1w images, followed by a second step where the lesions are segmented as outliers to the normal apparent GM brain tissue on the FLAIR image.

RESULTS

The tool has been validated using data from more than 100 MS patients acquired with different scanners and at different magnetic field strengths. Quantitative evaluation provided a better performance in terms of precision while maintaining similar results on sensitivity and Dice similarity measures compared with those of other approaches.

CONCLUSION

Our tool is implemented as a publicly available SPM8/12 extension that can be used by both the medical and research communities.},
 author = {Roura, Eloy and Oliver, Arnau and Cabezas, Mariano and Valverde, Sergi and Pareto, Deborah and Vilanova, Joan C. and Rami{\'o}-Torrent{\`a}, Llu{\'i}s and Rovira, {\`A}lex and Llad{\'o}, Xavier},
 year = {2015},
 title = {A toolbox for multiple sclerosis lesion segmentation},
 url = {https://pubmed.ncbi.nlm.nih.gov/26227167/},
 pages = {1031--1043},
 volume = {57},
 number = {10},
 issn = {1432-1920},
 journal = {Neuroradiology},
 doi = {10.1007/s00234-015-1552-2}
}


@article{Ruff.2021,
 abstract = {Deep learning approaches to anomaly detection (AD) have recently improved the state of the art in detection performance on complex data sets, such as large collections of images or text. These results have sparked a renewed interest in the AD problem and led to the introduction of a great variety of new methods. With the emergence of numerous such methods, including approaches based on generative models, one-class classification, and reconstruction, there is a growing need to bring methods of this field into a systematic and unified perspective. In this review, we aim to identify the common underlying principles and the assumptions that are often made implicitly by various methods. In particular, we draw connections between classic ``shallow'' and novel deep approaches and show how this relation might cross-fertilize or extend both directions. We further provide an empirical assessment of major existing methods that are enriched by the use of recent explainability techniques and present specific worked-through examples together with practical advice. Finally, we outline critical open challenges and identify specific paths for future research in AD.},
 author = {Ruff, Lukas and Kauffmann, Jacob R. and Vandermeulen, Robert A. and Montavon, Gregoire and Samek, Wojciech and Kloft, Marius and Dietterich, Thomas G. and Muller, Klaus-Robert},
 year = {2021},
 title = {A Unifying Review of Deep and Shallow Anomaly Detection},
 pages = {756--795},
 volume = {109},
 number = {5},
 issn = {1558-2256},
 journal = {Proceedings of the IEEE},
 doi = {10.1109/JPROC.2021.3052449}
}


@article{Saase.2020,
 abstract = {},
 author = {Saase, Victor and Wenz, Holger and Ganslandt, Thomas and Groden, Christoph and Maros, M{\'a}t{\'e} E.},
 year = {2020},
 title = {Simple statistical methods for unsupervised brain anomaly detection on MRI are competitive to deep learning methods},
 journal = {arXiv preprint arXiv:2011.12735}
}


@inproceedings{Sato.2018,
 abstract = {},
 author = {Sato, Daisuke and Hanaoka, Shouhei and Nomura, Yukihiro and Takenaga, Tomomi and Miki, Soichiro and Yoshikawa, Takeharu and Hayashi, Naoto and Abe, Osamu},
 title = {A primitive study on unsupervised anomaly detection with an autoencoder in emergency head CT volumes},
 pages = {60},
 publisher = {SPIE},
 isbn = {9781510616394},
 series = {Progress in biomedical optics and imaging},
 editor = {Petrick, Nicholas and Mori, Kensaku},
 booktitle = {Medical Imaging 2018: Computer-Aided Diagnosis},
 year = {2018},
 address = {Bellingham, Washington, USA},
 doi = {10.1117/12.2292276}
}


@inproceedings{Sato.2019,
 abstract = {},
 author = {Sato, Kazuki and Hama, Kenta and Matsubara, Takashi and Uehara, Kuniaki},
 title = {Predictable Uncertainty-Aware Unsupervised Deep Anomaly Segmentation},
 pages = {1--7},
 publisher = {IEEE},
 isbn = {978-1-7281-1985-4},
 booktitle = {2019 International Joint Conference on Neural Networks (IJCNN 2019)},
 year = {2019},
 address = {Piscataway, NJ},
 doi = {10.1109/IJCNN.2019.8852144}
}


@article{Scholkopf.1999,
 abstract = {},
 author = {Sch{\"o}lkopf, Bernhard and Williamson, Robert C. and Smola, Alex and Shawe-Taylor, John and Platt, John},
 year = {1999},
 title = {Support vector method for novelty detection},
 volume = {12},
 journal = {Advances in neural information processing systems}
}


@proceedings{Shen.2019,
 abstract = {},
 year = {2019},
 title = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13--17, 2019, Proceedings, Part IV},
 address = {Cham},
 edition = {1st ed. 2019},
 volume = {11767},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-030-32251-9},
 series = {Lecture Notes in Computer Science},
 editor = {Shen, Dinggang and Liu, Tianming and Peters, Terry M. and Staib, Lawrence H. and Essert, Caroline and Zhou, Sean},
 doi = {10.1007/978-3-030-32251-9}
}


@proceedings{ShenDinggangandLiuTianmingandPetersTerryM.andStaibLawrenceH.andEssertCarolinean.2019,
 abstract = {},
 year = {2019},
 title = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019},
 address = {Cham},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-030-32251-9},
 editor = {{Shen, Dinggang and Liu, Tianming and Peters, Terry M. and Staib, Lawrence H. and Essert, Caroline and Zhou, Sean and Yap, Pew-Thian and Khan, Ali}}
}


@inproceedings{Shin.2018,
 abstract = {Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models. In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI. We demonstrate two unique benefits that the synthetic images provide. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation. Second, we demonstrate the value of generative models as an anonymization tool, achieving comparable tumor segmentation results when trained on the synthetic data versus when trained on real subject data. Together, these results offer a potential solution to two of the largest challenges facing machine learning in medical imaging, namely the small incidence of pathological findings, and the restrictions around sharing of patient data.},
 author = {Shin, Hoo-Chang and Tenenholtz, Neil A. and Rogers, Jameson K. and Schwarz, Christopher G. and Senjem, Matthew L. and Gunter, Jeffrey L. and Andriole, Katherine P. and Michalski, Mark},
 title = {Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks},
 pages = {1--11},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-030-00536-8},
 editor = {Gooya, Ali and Goksel, Orcun and Oguz, Ipek and Burgos, Ninon},
 booktitle = {Simulation and Synthesis in Medical Imaging},
 year = {2018},
 address = {Cham}
}


@article{SilvaRodriguez.2022,
 abstract = {},
 author = {Silva-Rodr{\'i}guez, Julio and Naranjo, Valery and Dolz, Jose},
 year = {2022},
 title = {Constrained unsupervised anomaly segmentation},
 pages = {102526},
 volume = {80},
 issn = {1361-8415},
 journal = {Medical Image Analysis}
}


@article{Song.2009,
 abstract = {In this work we present a new support vector machine (SVM)-based method for fMRI data analysis. SVM has been shown to be a powerful, efficient data-driven tool in pattern recognition, and has been applied to the supervised classification of brain cognitive states in fMRI experiments. We examine the unsupervised mapping of activated brain regions using SVM. Specifically, the mapping process is formulated as an outlier detection problem of one-class SVM (OCSVM) that provides initial mapping results. These results are further refined by applying prototype selection and SVM reclassification. Multiple spatial and temporal features are extracted and selected to facilitate SVM learning. The proposed method was compared with correlation analysis (CA), t-test (TT), and spatial independent component analysis (SICA) methods using synthetic and experimental data. Our results show that the proposed method can provide more accurate and robust activation mapping than CA, TT and SICA, and is computationally more efficient than SICA. Besides its applicability to typical fMRI experiments, the proposed method is also a powerful tool in fMRI studies where a reliable quantification of activated brain regions is required.},
 author = {Song, Xiaomu and Wyrwicz, Alice M.},
 year = {2009},
 title = {Unsupervised spatiotemporal fMRI data analysis using support vector machines},
 url = {https://www.sciencedirect.com/science/article/pii/S1053811909002924},
 pages = {204--212},
 volume = {47},
 number = {1},
 issn = {1053-8119},
 journal = {NeuroImage},
 doi = {10.1016/j.neuroimage.2009.03.054}
}


@article{Souza.2018,
 abstract = {This paper presents an open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset, named Calgary-Campinas-359 (CC-359). The dataset is composed of images of older healthy adults (29-80 years) acquired on scanners from three vendors (Siemens, Philips and General Electric) at both 1.5~T and 3~T. CC-359 is comprised of 359 datasets, approximately 60 subjects per vendor and magnetic field strength. The dataset is approximately age and gender balanced, subject to the constraints of the available images. It provides consensus brain extraction masks for all volumes generated using supervised classification. Manual segmentation results for twelve randomly selected subjects performed by an expert are also provided. The CC-359 dataset allows investigation of 1) the influences of both vendor and magnetic field strength on quantitative analysis of brain MR; 2) parameter optimization for automatic segmentation methods; and potentially 3) machine learning classifiers with big data, specifically those based on deep learning methods, as these approaches require a large amount of data. To illustrate the utility of this dataset, we compared to the results of a supervised classifier, the results of eight publicly available skull stripping methods and one publicly available consensus algorithm. A linear mixed effects model analysis indicated that vendor (p-value{\textless}0.001) and magnetic field strength (p-value{\textless}0.001) have statistically significant impacts on skull stripping results.},
 author = {Souza, Roberto and Lucena, Oeslle and Garrafa, Julia and Gobbi, David and Saluzzi, Marina and Appenzeller, Simone and Rittner, Let{\'i}cia and Frayne, Richard and Lotufo, Roberto},
 year = {2018},
 title = {An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement},
 pages = {482--494},
 volume = {170},
 issn = {1053-8119},
 journal = {NeuroImage},
 doi = {10.1016/j.neuroimage.2017.08.021}
}


@proceedings{Springer.2015,
 abstract = {},
 year = {2015},
 title = {Medical Image Computing and Computer Assisted Intervention},
 institution = {Springer}
}


@article{Sudlow.2015,
 abstract = {Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.},
 author = {Sudlow, Cathie and Gallacher, John and Allen, Naomi and Beral, Valerie and Burton, Paul and Danesh, John and Downey, Paul and Elliott, Paul and Green, Jane and Landray, Martin and Liu, Bette and Matthews, Paul and Ong, Giok and Pell, Jill and Silman, Alan and Young, Alan and Sprosen, Tim and Peakman, Tim and Collins, Rory},
 year = {2015},
 title = {UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age},
 pages = {e1001779},
 volume = {12},
 number = {3},
 journal = {PLoS medicine}
}


@inproceedings{T.Haeck.2019,
 abstract = {We present a fully-automated MRI brain tumor segmentation method that does not require any manually annotated training data. The method is independent of the scanner or acquisition protocol and is directly applicable to any individual patient image. An Expectation Maximization-approach is used to estimate intensity models for both normal and tumorous tissue. The segmentation is represented by a level-set that is iteratively updated to label voxels as normal or tumorous, based on which intensity model explains the voxels' intensity the best. The method is compared with the method by Menze et al. [1], which is considered to be a benchmark for unsupervised tumor segmentation. The performance of our method for segmenting the tumor volume is summarized by an average Dice score of 0.87 $\pm$ 0.06 on the training data set of the MICCAI BraTS Challenge 2012--2013.},
 author = { Haeck, T. and  Maes, F. and  Suetens, P.},
 title = {An untrained and unsupervised method for MRI brain tumor segmentation},
 pages = {265--268},
 isbn = {1945-8452},
 booktitle = {2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
 year = {2019},
 doi = {10.1109/ISBI.2016.7493260}
}


@inproceedings{T.Samaille.2012,
 abstract = {SPIE Digital Library Proceedings},
 author = {Samaille, T. and Colliot, O. and Cuingnet, R. and Jouvent, E. and Chabriat, H. and Dormont, D. and Chupin, M.},
 title = {Automatic segmentation of white matter hyperintensities robust to multicentre acquisition and pathological variability},
 url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/8314/1/Automatic-segmentation-of-white-matter-hyperintensities-robust-to-multicentre-acquisition/10.1117/12.910268.full},
 pages = {1178--1186},
 publisher = {SPIE},
 year = {2012},
 doi = {10.1117/12.910268}
}


@inproceedings{Tan.2021,
 abstract = {Supervised learning of every possible pathology is unrealistic for many primary care applications like health screening. Image anomaly detection methods that learn normal appearance from only healthy data have shown promising results recently. We propose an...},
 author = {Tan, Jeremy and Hou, Benjamin and Day, Thomas and Simpson, John and Rueckert, Daniel and Kainz, Bernhard},
 title = {Detecting Outliers with Poisson Image Interpolation},
 url = {https://link.springer.com/chapter/10.1007/978-3-030-87240-3_56},
 pages = {581--591},
 publisher = {{Springer, Cham}},
 year = {2021},
 doi = {10.1007/978-3-030-87240-3{\textunderscore }56}
}


@article{Tan.2022,
 abstract = {In medical imaging, outliers can contain hypo/hyper-intensities, minor deformations, or completely altered anatomy. To detect these irregularities it is helpful to learn the features present in both normal and abnormal images. However this is difficult because of the wide range of possible abnormalities and also the number of ways that normal anatomy can vary naturally. As such, we leverage the natural variations in normal anatomy to create a range of synthetic abnormalities. Specifically, the same patch region is extracted from two independent samples and replaced with an interpolation between both patches. The interpolation factor, patch size, and patch location are randomly sampled from uniform distributions. A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor. This encourages the network to learn what features to expect normally and to identify where foreign patterns have been introduced. The estimate of the interpolation factor lends itself nicely to the derivation of an outlier score. Meanwhile the pixel-wise output allows for pixel- and subject- level predictions using the same model.},
 author = {Tan, Jeremy and Hou, Benjamin and Batten, James and Qiu, Huaqi and Kainz, Bernhard},
 year = {2022},
 title = {Detecting Outliers with Foreign Patch Interpolation},
 url = {https://www.melba-journal.org/papers/2022:013.html},
 pages = {1--27},
 volume = {1},
 number = {April 2022 issue},
 issn = {2766-905X},
 journal = {Machine Learning for Biomedical Imaging}
}


@inproceedings{Tan.2022b,
 abstract = {Using self-supervision in anomaly detection can increase sensitivity to subtle irregularities. However, increasing sensitivity to certain classes of outliers could result in decreased sensitivity to other types. While a single model may have limited coverage, an...},
 author = {Tan, Jeremy and Kart, Turkay and Hou, Benjamin and Batten, James and Kainz, Bernhard},
 title = {MetaDetector: Detecting Outliers by Learning to Learn from Self-supervision},
 url = {https://link.springer.com/chapter/10.1007/978-3-030-97281-3_18},
 pages = {119--126},
 publisher = {{Springer, Cham}},
 year = {2022},
 doi = {10.1007/978-3-030-97281-3{\textunderscore }18}
}


@article{Taylor.2017,
 abstract = {This paper describes the data repository for the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) initial study cohort. The Cam-CAN Stage 2 repository contains multi-modal (MRI, MEG, and cognitive-behavioural) data from a large (approximately N=700), cross-sectional adult lifespan (18-87years old) population-based sample. The study is designed to characterise age-related changes in cognition and brain structure and function, and to uncover the neurocognitive mechanisms that support healthy cognitive ageing. The database contains raw and preprocessed structural MRI, functional MRI (active tasks and resting state), and MEG data (active tasks and resting state), as well as derived scores from cognitive behavioural experiments spanning five broad domains (attention, emotion, action, language, and memory), and demographic and neuropsychological data. The dataset thus provides a depth of neurocognitive phenotyping that is currently unparalleled, enabling integrative analyses of age-related changes in brain structure, brain function, and cognition, and providing a testbed for novel analyses of multi-modal neuroimaging data.},
 author = {Taylor, Jason R. and Williams, Nitin and Cusack, Rhodri and Auer, Tibor and Shafto, Meredith A. and Dixon, Marie and Tyler, Lorraine K. and Cam-Can and Henson, Richard N.},
 year = {2017},
 title = {The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample},
 pages = {262--269},
 volume = {144},
 number = {Pt B},
 issn = {1053-8119},
 journal = {NeuroImage}
}


@article{Tschuchnig.2022,
 abstract = {The increasing digitization of medical imaging enables machine learning based improvements in detecting, visualizing and segmenting lesions, easing the workload for medical experts. However, supervised machine learning requires reliable labelled data, which is is often difficult or impossible to collect or at least time consuming and thereby costly. Therefore methods requiring only partly labeled data (semi-supervised) or no labeling at all (unsupervised methods) have been applied more regularly. Anomaly detection is one possible methodology that is able to leverage semi-supervised and unsupervised methods to handle medical imaging tasks like classification and segmentation. This paper uses a semi-exhaustive literature review of relevant anomaly detection papers in medical imaging to cluster into applications, highlight important results, establish lessons learned and give further advice on how to approach anomaly detection in medical imaging. The qualitative analysis is based on google scholar and 4 different search terms, resulting in 120 different analysed papers. The main results showed that the current research is mostly motivated by reducing the need for labelled data. Also, the successful and substantial amount of research in the brain MRI domain shows the potential for applications in further domains like OCT and chest X-ray.},
 author = {Tschuchnig, Maximilian E. and Gadermayr, Michael},
 year = {2022},
 title = {Anomaly Detection in Medical Imaging -- A Mini Review},
 keywords = {Computer Science - Computer Vision and Pattern Recognition;Computer Science - Learning},
 pages = {33--38},
 doi = {10.1007/978-3-658-36295-9{\textunderscore }5}
}


@article{Uzunova.2019,
 abstract = {PURPOSE

Pathology detection in medical image data is an important but a rather complicated task. In particular, the big variability of the pathologies is a challenge to automatic detection methods and even to machine learning methods. Supervised algorithms would usually learn the appearance of a single pathological structure based on a large annotated dataset. As such data is not usually available, especially in large amounts, in this work we pursue a different unsupervised approach.

METHODS

Our method is based on learning the entire variability of healthy data and detect pathologies by their differences to the learned norm. For this purpose, we use conditional variational autoencoders which learn the reconstruction and encoding distribution of healthy images and also have the ability to integrate certain prior knowledge about the data (condition).

RESULTS

Our experiments on different 2D and 3D datasets show that the approach is suitable for the detection of pathologies and deliver reasonable Dice coefficients and AUCs. Also this method can estimate missing correspondences in pathological images and thus can be used as a pre-step to a registration method. Our experiments show improving registration results on pathological data when using this approach.

CONCLUSIONS

Overall the presented approach is suitable for a rough pathology detection in medical images and can be successfully used as a preprocessing step to other image processing methods.},
 author = {Uzunova, Hristina and Schultz, Sandra and Handels, Heinz and Ehrhardt, Jan},
 year = {2019},
 title = {Unsupervised pathology detection in medical images using conditional variational autoencoders},
 keywords = {Algorithms;Area Under Curve;Brain Neoplasms/diagnostic imaging;Glioblastoma/diagnostic imaging;Humans;Image Processing, Computer-Assisted/methods;Imaging, Three-Dimensional/methods;Lung/diagnostic imaging;Machine Learning;Pathology/methods;Phantoms, Imaging},
 pages = {451--461},
 volume = {14},
 number = {3},
 journal = {International journal of computer assisted radiology and surgery},
 doi = {10.1007/s11548-018-1898-0}
}


@article{vanEssen.2012,
 abstract = {The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.},
 author = {{van Essen}, D. C. and Ugurbil, K. and Auerbach, E. and Barch, D. and Behrens, T. E. J. and Bucholz, R. and Chang, A. and Chen, L. and Corbetta, M. and Curtiss, S. W. and {Della Penna}, S. and Feinberg, D. and Glasser, M. F. and Harel, N. and Heath, A. C. and Larson-Prior, L. and Marcus, D. and Michalareas, G. and Moeller, S. and Oostenveld, R. and Petersen, S. E. and Prior, F. and Schlaggar, B. L. and Smith, S. M. and Snyder, A. Z. and Xu, J. and Yacoub, E.},
 year = {2012},
 title = {The Human Connectome Project: a data acquisition perspective},
 pages = {2222--2231},
 volume = {62},
 number = {4},
 issn = {1053-8119},
 journal = {NeuroImage},
 doi = {10.1016/j.neuroimage.2012.02.018}
}


@article{vanHespen.2021,
 abstract = {The performance of current machine learning methods to detect heterogeneous pathology is limited by the quantity and quality of pathology in medical images. A possible solution is anomaly detection; an approach that can detect all abnormalities by learning how 'normal' tissue looks like. In this work, we propose an anomaly detection method using a neural network architecture for the detection of chronic brain infarcts on brain MR images. The neural network was trained to learn the visual appearance of normal appearing brains of 697 patients. We evaluated its performance on the detection of chronic brain infarcts in 225 patients, which were previously labeled. Our proposed method detected 374 chronic brain infarcts (68{\%} of the total amount of brain infarcts) which represented 97.5{\%} of the total infarct volume. Additionally, 26 new brain infarcts were identified that were originally missed by the radiologist during radiological reading. Our proposed method also detected white matter hyperintensities, anomalous calcifications, and imaging artefacts. This work shows that anomaly detection is a powerful approach for the detection of multiple brain abnormalities, and can potentially be used to improve the radiological workflow efficiency by guiding radiologists to brain anomalies which otherwise remain unnoticed.},
 author = {{van Hespen}, Kees M. and Zwanenburg, Jaco J. M. and Dankbaar, Jan W. and Geerlings, Mirjam I. and Hendrikse, Jeroen and Kuijf, Hugo J.},
 year = {2021},
 title = {An anomaly detection approach to identify chronic brain infarcts on MRI},
 url = {https://www.nature.com/articles/s41598-021-87013-4},
 pages = {7714},
 volume = {11},
 number = {1},
 issn = {2045-2322},
 journal = {Scientific Reports},
 doi = {10.1038/s41598-021-87013-4}
}


@article{vanLeemput.2001,
 abstract = {This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimates tissue-specific intensity models from the data itself, and incorporates contextual information in the classification using a Markov random field. The results of the automated method are compared with lesion delineations by human experts, showing a high total lesion load correlation. When the degree of spatial correspondence between segmentations is taken into account, considerable disagreement is found, both between expert segmentations, and between expert and automatic measurements.},
 author = {{van Leemput}, K. and Maes, F. and Vandermeulen, D. and Colchester, A. and Suetens, P.},
 year = {2001},
 title = {Automated segmentation of multiple sclerosis lesions by model outlier detection},
 url = {https://pubmed.ncbi.nlm.nih.gov/11513020/},
 pages = {677--688},
 volume = {20},
 number = {8},
 issn = {0278-0062},
 journal = {IEEE transactions on medical imaging},
 doi = {10.1109/42.938237}
}


@article{Vernooij.2007,
 abstract = {BACKGROUND

Magnetic resonance imaging (MRI) of the brain is increasingly used both in research and in clinical medicine, and scanner hardware and MRI sequences are continually being improved. These advances are likely to result in the detection of unexpected, asymptomatic brain abnormalities, such as brain tumors, aneurysms, and subclinical vascular pathologic changes. We conducted a study to determine the prevalence of such incidental brain findings in the general population.

METHODS

The subjects were 2000 persons (mean age, 63.3 years; range, 45.7 to 96.7) from the population-based Rotterdam Study in whom high-resolution, structural brain MRI (1.5 T) was performed according to a standardized protocol. Two trained reviewers recorded all brain abnormalities, including asymptomatic brain infarcts. The volume of white-matter lesions was quantified in milliliters with the use of automated postprocessing techniques. Two experienced neuroradiologists reviewed all incidental findings. All diagnoses were based on MRI findings, and additional histologic confirmation was not obtained.

RESULTS

Asymptomatic brain infarcts were present in 145 persons (7.2{\%}). Among findings other than infarcts, cerebral aneurysms (1.8{\%}) and benign primary tumors (1.6{\%}), mainly meningiomas, were the most frequent. The prevalence of asymptomatic brain infarcts and meningiomas increased with age, as did the volume of white-matter lesions, whereas aneurysms showed no age-related increase in prevalence.

CONCLUSIONS

Incidental brain findings on MRI, including subclinical vascular pathologic changes, are common in the general population. The most frequent are brain infarcts, followed by cerebral aneurysms and benign primary tumors. Information on the natural course of these lesions is needed to inform clinical management.},
 author = {Vernooij, Meike W. and Ikram, M. Arfan and Tanghe, Herv{\'e} L. and Vincent, Arnaud J. P. E. and Hofman, Albert and Krestin, Gabriel P. and Niessen, Wiro J. and Breteler, Monique M. B. and {van der Lugt}, Aad},
 year = {2007},
 title = {Incidental findings on brain MRI in the general population},
 pages = {1821--1828},
 volume = {357},
 number = {18},
 journal = {The New England journal of medicine},
 doi = {10.1056/NEJMoa070972}
}


@inproceedings{Vincent.2008,
 abstract = {},
 author = {Vincent, Pascal and Larochelle, Hugo and Bengio, Yoshua and Manzagol, Pierre-Antoine},
 title = {Extracting and composing robust features with denoising autoencoders},
 pages = {1096--1103},
 publisher = {ACM},
 isbn = {9781605582054},
 editor = {Cohen, William},
 booktitle = {Proceedings of the 25th international conference on Machine learning},
 year = {2008},
 address = {New York, NY},
 doi = {10.1145/1390156.1390294}
}


@article{Wang.2014,
 abstract = {White matter lesions (WMLs) are commonly observed on the magnetic resonance (MR) images of normal elderly in association with vascular risk factors, such as hypertension or stroke. An accurate WML detection provides significant information for disease tracking, therapy evaluation, and normal aging research. In this article, we present an unsupervised WML segmentation method that uses Gaussian mixture model to describe the intensity distribution of the normal brain tissues and detects the WMLs as outliers to the normal brain tissue model based on extreme value theory. The detection of WMLs is performed by comparing the probability distribution function of a one-sided normal distribution and a Gumbel distribution, which is a specific extreme value distribution. The performance of the automatic segmentation is validated on synthetic and clinical MR images with regard to different imaging sequences and lesion loads. Results indicate that the segmentation method has a favorable accuracy competitive with other state-of-the-art WML segmentation methods.},
 author = {Wang, Rui and Li, Chao and Wang, Jie and Wei, Xiaoer and Li, Yuehua and Hui, Chun and Zhu, Yuemin and Zhang, Su},
 year = {2014},
 title = {Automatic segmentation of white matter lesions on magnetic resonance images of the brain by using an outlier detection strategy},
 url = {https://www.sciencedirect.com/science/article/pii/S0730725X14002409},
 pages = {1321--1329},
 volume = {32},
 number = {10},
 issn = {0730-725X},
 journal = {Magnetic Resonance Imaging},
 doi = {10.1016/j.mri.2014.08.010}
}


@proceedings{Wang.2022,
 abstract = {},
 year = {2022},
 title = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022},
 address = {Cham},
 volume = {13438},
 publisher = {{Springer Nature Switzerland}},
 editor = {Wang, Linwei and Dou, Qi and Fletcher, P. Thomas and Speidel, Stefanie and Li, Shuo},
 doi = {10.1007/978-3-031-16452-1}
}


@article{Watkins.2001,
 abstract = {The use of computational approaches in the analysis of high resolution magnetic resonance images (MRI) of the human brain provides a powerful tool for in vivo studies of brain anatomy. Here, we report results obtained with a voxel-wise statistical analysis of hemispheric asymmetries in regional 'amounts' of gray matter, based on MRI scans obtained in 142 healthy young adults. Firstly, the voxel-wise analysis detected the well-known frontal (right {\textgreater} left) and occipital (left {\textgreater} right) petalias. Secondly, our analysis confirmed the presence of left-greater-than-right asymmetries in several posterior language areas, including the planum temporale and the angular gyrus; no significant asymmetry was detected in the anterior language regions. We also found previously described asymmetries in the cingulate sulcus (right {\textgreater} left) and the caudate nucleus (right {\textgreater} left). Finally, in some brain regions we observed highly significant asymmetries that were not reported before, such as in the anterior insular cortex (right {\textgreater} left). The above asymmetries were observed in men and women. Our results thus provide confirmation of the known structural asymmetries in the human brain as well as new findings that may stimulate further research of hemispheric specialization.},
 author = {Watkins, K. E. and Paus, T. and Lerch, J. P. and Zijdenbos, A. and Collins, D. L. and Neelin, P. and Taylor, J. and Worsley, K. J. and Evans, A. C.},
 year = {2001},
 title = {Structural asymmetries in the human brain: a voxel-based statistical analysis of 142 MRI scans},
 keywords = {Adolescent;Adult;Caudate Nucleus/anatomy {\&} histology;Cerebral Cortex/anatomy {\&} histology;Chi-Square Distribution;Data Interpretation, Statistical;Female;Functional Laterality;Gyrus Cinguli/anatomy {\&} histology;Humans;Magnetic Resonance Imaging/methods/statistics {\&} numerical data;Male},
 pages = {868--877},
 volume = {11},
 number = {9},
 issn = {1047-3211},
 journal = {Cerebral cortex (New York, N.Y. : 1991)},
 doi = {10.1093/cercor/11.9.868}
}


@article{Weiner.2017,
 abstract = {INTRODUCTION

The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI-3, which began on August 1, 2016, is a 5-year renewal of the current ADNI-2 study.

METHODS

ADNI-3 will follow current and additional subjects with normal cognition, mild cognitive impairment, and AD using innovative technologies such as tau imaging, magnetic resonance imaging sequences for connectivity analyses, and a highly automated immunoassay platform and mass spectroscopy approach for cerebrospinal fluid biomarker analysis. A Systems Biology/pathway approach will be used to identify genetic factors for subject selection/enrichment. Amyloid positron emission tomography scanning will be standardized using the Centiloid method. The Brain Health Registry will help recruit subjects and monitor subject cognition.

RESULTS

Multimodal analyses will provide insight into AD pathophysiology and disease progression.

DISCUSSION

ADNI-3 will aim to inform AD treatment trials and facilitate development of AD disease-modifying treatments.},
 author = {Weiner, Michael W. and Veitch, Dallas P. and Aisen, Paul S. and Beckett, Laurel A. and Cairns, Nigel J. and Green, Robert C. and Harvey, Danielle and Jack, Clifford R. and Jagust, William and Morris, John C. and Petersen, Ronald C. and Salazar, Jennifer and Saykin, Andrew J. and Shaw, Leslie M. and Toga, Arthur W. and Trojanowski, John Q.},
 year = {2017},
 title = {The Alzheimer's Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement},
 pages = {561--571},
 volume = {13},
 number = {5},
 journal = {Alzheimer's {\&} dementia : the journal of the Alzheimer's Association}
}


@inproceedings{Wolleb.2022,
 abstract = {},
 author = {Wolleb, Julia and Bieder, Florentin and Sandk{\"u}hler, Robin and Cattin, Philippe C.},
 title = {Diffusion Models for Medical Anomaly Detection},
 pages = {35--45},
 volume = {13438},
 publisher = {{Springer Nature Switzerland}},
 editor = {Wang, Linwei and Dou, Qi and Fletcher, P. Thomas and Speidel, Stefanie and Li, Shuo},
 booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022},
 year = {2022},
 address = {Cham},
 doi = {10.1007/978-3-031-16452-1{\textunderscore }4}
}


@article{Woolard.2012,
 abstract = {Hemispheric asymmetry of the human hippocampus is well established, but poorly understood. We studied 110 healthy subjects with 3-Tesla MRI to explore the anatomical and functional correlates of the R{\textgreater}L volume asymmetry. We found that the asymmetry is limited to the anterior hippocampus (hemisphere$\times$region interaction: F(1,109)=42.6, p{\textless}.001). Anterior hippocampal volume was correlated strongly with the volumes of all four cortical lobes. In contrast, posterior hippocampal volume was correlated strongly only with occipital lobe volume, moderately with the parietal and temporal lobe volumes and not with the frontal lobe volume. The degree of R{\textgreater}L anterior hippocampal volume asymmetry predicted performance on a measure of basic cognitive abilities. This provides evidence for regional specificity and functional implications of the well-known hemispheric asymmetry of hippocampal volume. We suggest that the developmental profile, genetic mechanisms and functional implications of R{\textgreater}L anterior hippocampal volume asymmetry in the human brain deserve further study.},
 author = {Woolard, Austin A. and Heckers, Stephan},
 year = {2012},
 title = {Anatomical and functional correlates of human hippocampal volume asymmetry},
 keywords = {Adult;Female;Functional Laterality/physiology;Hippocampus/anatomy {\&} histology/physiology;Humans;Image Processing, Computer-Assisted;Magnetic Resonance Imaging;Male;Neuropsychological Tests;Organ Size/physiology},
 pages = {48--53},
 volume = {201},
 number = {1},
 issn = {0165-1781},
 journal = {Psychiatry research},
 doi = {10.1016/j.pscychresns.2011.07.016}
}


@inproceedings{Wyatt.2022,
 abstract = {},
 author = {Wyatt, Julian and Leach, Adam and Schmon, Sebastian M. and Willcocks, Chris G.},
 title = {Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise},
 pages = {650--656},
 year = {2022},
 booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}
}


@inproceedings{Y.Zhou.2019,
 abstract = {There is an increasing need for efficient and automatic evaluation of brain tumors on magnetic resonance images (MRI). Most of the previous works focus on segmentation, registration, and growth modeling of the most common primary brain tumor gliomas, or the classification of up to three types of brain tumors. In this work, we extend the study to eight types of brain tumors where only global diagnosis labels are given but not the slice-level labels. We propose a weakly supervised method and demonstrate that inferring disease types at the slice-level would help the global label prediction. We also provide an algorithm for feature extraction via randomly choosing connection paths through class-specific autoencoders with dropout to accommodate the small-dataset problem. Experimental results on both public and proprietary datasets are compared to the baseline methods. The classification with the weakly supervised setting on the proprietary data, consisting of 295 patients with eight different tumor types, shows close results to the upper bound in the supervised learning setting.},
 author = {Zhou, Y. and Li, Z. and Ma, C. and Chen, C. and Gao, M. and Zhu, H. and Xu, J.},
 title = {Weakly-Supervised Brain Tumor Classification with Global Diagnosis Label},
 pages = {1--5},
 isbn = {1945-8452},
 booktitle = {2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
 year = {2019},
 doi = {10.1109/ISBI45749.2020.9098640}
}


@inproceedings{Ye.2013,
 abstract = {We propose a general database-driven framework for coherent synthesis of subject-specific scans of desired modality, which adopts and generalizes the patch-based label propagation (LP) strategy. While modality synthesis has received increased attention lately,...},
 author = {Ye, Dong Hye and Zikic, Darko and Glocker, Ben and Criminisi, Antonio and Konukoglu, Ender},
 title = {Modality Propagation: Coherent Synthesis of Subject-Specific Scans with Data-Driven Regularization},
 url = {https://link.springer.com/chapter/10.1007/978-3-642-40811-3_76},
 pages = {606--613},
 publisher = {{Springer, Berlin, Heidelberg}},
 year = {2013},
 doi = {10.1007/978-3-642-40811-3{\textunderscore }76}
}


@article{Yoo.2018,
 abstract = {Myelin imaging is a form of quantitative magnetic resonance imaging (MRI) that measures myelin content and can potentially allow demyelinating diseases such as multiple sclerosis (MS) to be detected earlier. Although focal lesions are the most visible signs of MS pathology on conventional MRI, it has been shown that even tissues that appear normal may exhibit decreased myelin content as revealed by myelin-specific images (i.e., myelin maps). Current methods for analyzing myelin maps typically use global or regional mean myelin measurements to detect abnormalities, but ignore finer spatial patterns that may be characteristic of MS. In this paper, we present a machine learning method to automatically learn, from multimodal MR images, latent spatial features that can potentially improve the detection of MS pathology at early stage. More specifically, 3D image patches are extracted from myelin maps and the corresponding T1-weighted (T1w) MRIs, and are used to learn a latent joint myelin-T1w feature representation via unsupervised deep learning. Using a data set of images from MS patients and healthy controls, a common set of patches are selected via a voxel-wise t-test performed between the two groups. In each MS image, any patches overlapping with focal lesions are excluded, and a feature imputation method is used to fill in the missing values. A feature selection process (LASSO) is then utilized to construct a sparse representation. The resulting normal-appearing features are used to train a random forest classifier. Using the myelin and T1w images of 55 relapse-remitting MS patients and 44 healthy controls in an 11-fold cross-validation experiment, the proposed method achieved an average classification accuracy of 87.9{\%} (SD~=~8.4{\%}), which is higher and more consistent across folds than those attained by regional mean myelin (73.7{\%}, SD~=~13.7{\%}) and T1w measurements (66.7{\%}, SD~=~10.6{\%}), or deep-learned features in either the myelin (83.8{\%}, SD~=~11.0{\%}) or T1w (70.1{\%}, SD~=~13.6{\%}) images alone, suggesting that the proposed method has strong potential for identifying image features that are more sensitive and specific to MS pathology in normal-appearing brain tissues.},
 author = {Yoo, Youngjin and Tang, Lisa Y. W. and Brosch, Tom and Li, David K. B. and Kolind, Shannon and Vavasour, Irene and Rauscher, Alexander and MacKay, Alex L. and Traboulsee, Anthony and Tam, Roger C.},
 year = {2018},
 title = {Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls},
 pages = {169--178},
 volume = {17},
 issn = {2213-1582},
 journal = {NeuroImage: Clinical},
 doi = {10.1016/j.nicl.2017.10.015}
}


@inproceedings{You.2019,
 abstract = {While human experts excel in and rely on identifying an abnormal structure when assessing a medical scan, without necessarily specifying the type, current unsupervised abnormality detection methods are far from being practical. Recently proposed deep-learning (DL) based methods were initial attempts at showing the capabilities of this approach. In this work, we propose an outlier detection method combining image restoration with unsupervised learning based on DL. A normal anatomy prior is learned by training a Gaussian Mixture Variational Auto-Encoder (GMVAE) on images from healthy individuals. This prior is then used in a Maximum-A-Posteriori (MAP) restoration model to detect outliers. Abnormal lesions, not represented in the prior, are removed from the images during restoration to satisfy the prior and the difference between original and restored images form the detection of the method. We evaluated the proposed method on Magnetic Resonance Images (MRI) of patients with brain tumors and compared against previous baselines. Experimental results indicate that the method is capable of detecting lesions in the brain and achieves improvement over the current state of the art.},
 author = {You, Suhang and Tezcan, Kerem C. and Chen, Xiaoran and Konukoglu, Ender},
 title = {Unsupervised Lesion Detection via Image Restoration with a Normative Prior},
 url = {https://proceedings.mlr.press/v102/you19a.html},
 pages = {540--556},
 volume = {102},
 publisher = {PMLR},
 series = {Proceedings of Machine Learning Research},
 editor = {Cardoso, M. Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom},
 booktitle = {Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning},
 year = {2019}
}


@inproceedings{YuchenXie.2011,
 abstract = {SPIE Digital Library Proceedings},
 author = {Xie, Yuchen and Tao, Xiaodong},
 title = {White matter lesion segmentation using machine learning and weakly labeled MR images},
 url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/7962/79622G/White-matter-lesion-segmentation-using-machine-learning-and-weakly-labeled/10.1117/12.878237.full},
 pages = {778--786},
 publisher = {SPIE},
 year = {2011},
 doi = {10.1117/12.878237}
}


@inproceedings{Zavrtanik.2021,
 abstract = {},
 author = {Zavrtanik, Vitjan and Kristan, Matej and Sko{\v{c}}aj, Danijel},
 title = {Draem-a discriminatively trained reconstruction embedding for surface anomaly detection},
 pages = {8330--8339},
 booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
 year = {2021}
}


@book{Zbontar.2019,
 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.},
 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.},
 year = {2019},
 title = {fastMRI: An Open Dataset and Benchmarks for Accelerated MRI}
}


@inproceedings{Zimmerer.2019,
 abstract = {An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning and especially Variational Auto-Encoders (VAEs) have shown great potential in the unsupervised learning of data distributions. In principle, this allows for such a check and even the localization of parts in the image that are most suspicious. Currently, however, the reconstruction-based localization by design requires adjusting the model architecture to the specific problem looked at during evaluation. This contradicts the principle of building assumption-free models. We propose complementing the localization part with a term derived from the Kullback-Leibler (KL)-divergence. For validation, we perform a series of experiments on FashionMNIST as well as on a medical task including {\textgreater}1000 healthy and {\textgreater}250 brain tumor patients. Results show that the proposed formalism outperforms the state of the art VAE-based localization of anomalies across many hyperparameter settings and also shows a competitive max performance.},
 author = {Zimmerer, David and Isensee, Fabian and Petersen, Jens and Kohl, Simon and Maier-Hein, Klaus},
 title = {Unsupervised Anomaly Localization Using Variational Auto-Encoders},
 pages = {289--297},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-030-32251-9},
 editor = {{Shen, Dinggang and Liu, Tianming and Peters, Terry M. and Staib, Lawrence H. and Essert, Caroline and Zhou, Sean and Yap, Pew-Thian and Khan, Ali}},
 booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019},
 year = {2019},
 address = {Cham}
}


@inproceedings{Zimmerer.2019b,
 abstract = {Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based autoencoders have shown great potential in detecting anomalies in medical images. However, especially Variational Autoencoders (VAEs)often fail to capture the high-level structure in the data. We address these shortcomings by proposing the context-encoding Variational Autoencoder (ceVAE), which improves both, the sample, as well as pixelwise results. In our experiments on the BraTS-2017 and ISLES-2015 segmentation benchmarks the ceVAE achieves unsupervised AUROCs of 0.95 and 0.89, respectively, thus outperforming other reported deep-learning based approaches.},
 author = {Zimmerer, David and Kohl, Simon and Petersen, Jens and Isensee, Fabian and Maier-Hein, Klaus},
 title = {Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection},
 url = {https://arxiv.org/pdf/1907.12258},
 volume = {102},
 publisher = {PMLR},
 series = {Proceedings of Machine Learning Research},
 booktitle = {International Conference on Medical Imaging with Deep Learning (MIDL)},
 year = {2019}
}


@article{Zimmerer.2022,
 abstract = {Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they often produce incorrect and over-confident predictions. OoD detection algorithms aim to catch erroneous predictions in advance by analysing the data distribution and detecting potential instances of failure. Moreover, flagging OoD cases may support human readers in identifying incidental findings. Due to the increased interest in OoD algorithms, benchmarks for different domains have recently been established. In the medical imaging domain, for which reliable predictions are often essential, an open benchmark has been missing. We introduce the Medical-Out-Of-Distribution-Analysis-Challenge (MOOD) as an open, fair, and unbiased benchmark for OoD methods in the medical imaging domain. The analysis of the submitted algorithms shows that performance has a strong positive correlation with the perceived difficulty, and that all algorithms show a high variance for different anomalies, making it yet hard to recommend them for clinical practice. We also see a strong correlation between challenge ranking and performance on a simple toy test set, indicating that this might be a valuable addition as a proxy dataset during anomaly detection algorithm development.},
 author = {Zimmerer, David and Full, Peter M. and Isensee, Fabian and Jager, Paul and Adler, Tim and Petersen, Jens and Kohler, Gregor and Ross, Tobias and Reinke, Annika and Kascenas, Antanas and Jensen, Bjorn Sand and O'Neil, Alison Q. and Tan, Jeremy and Hou, Benjamin and Batten, James and Qiu, Huaqi and Kainz, Bernhard and Shvetsova, Nina and Fedulova, Irina and Dylov, Dmitry V. and Yu, Baolun and Zhai, Jianyang and Hu, Jingtao and Si, Runxuan and Zhou, Sihang and Wang, Siqi and Li, Xinyang and Chen, Xuerun and Zhao, Yang and Marimont, Sergio Naval and Tarroni, Giacomo and Saase, Victor and Maier-Hein, Lena and Maier-Hein, Klaus},
 year = {2022},
 title = {MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images},
 keywords = {Algorithms;Benchmarking;Humans;Machine Learning},
 pages = {2728--2738},
 volume = {41},
 number = {10},
 issn = {0278-0062},
 journal = {IEEE transactions on medical imaging},
 doi = {10.1109/TMI.2022.3170077}
}



@article{Schlegl.2019,
 author = {Schlegl, Thomas and Seeb{\"o}ck, Philipp and Waldstein, Sebastian M. and Langs, Georg and Schmidt-Erfurth, Ursula},
 year = {2019},
 title = {{f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks}},
 pages = {30--44},
 volume = {54},
 journal = {{Medical image analysis}},
 abstract = {Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-consuming. Furthermore, not all possibly relevant markers may be known and sufficiently well described a priori to even guide annotation. While supervised learning yields good results if expert labeled training data is available, the visual variability, and thus the vocabulary of findings, we can detect and exploit, is limited to the annotated lesions. Here, we present fast AnoGAN (f-AnoGAN), a generative adversarial network (GAN) based unsupervised learning approach capable of identifying anomalous images and image segments, that can serve as imaging biomarker candidates. We build a generative model of healthy training data, and propose and evaluate a fast mapping technique of new data to the GAN's latent space. The mapping is based on a trained encoder, and anomalies are detected via a combined anomaly score based on the building blocks of the trained model - comprising a discriminator feature residual error and an image reconstruction error. In the experiments on optical coherence tomography data, we compare the proposed method with alternative approaches, and provide comprehensive empirical evidence that f-AnoGAN outperforms alternative approaches and yields high anomaly detection accuracy. In addition, a visual Turing test with two retina experts showed that the generated images are indistinguishable from real normal retinal OCT images. The f-AnoGAN code is available at https://github.com/tSchlegl/f-AnoGAN.}
}


@inproceedings{Schlegl.2017,
 author = {Schlegl, Thomas and Seeb{\"o}ck, Philipp and Waldstein, Sebastian M. and Schmidt-Erfurth, Ursula and Langs, Georg},
 title = {{Unsupervised Anomaly Detection with Generative Adversarial Networks to  Guide Marker Discovery}},
 url = {https://arxiv.org/pdf/1703.05921},
 pages = {146--157},
 publisher = {{Springer International Publishing}},
 booktitle = {{The 25th biennial international conference on Information Processing in Medical Imaging (IPMI)}},
 year = {2017},
 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.}
}

@inproceedings{Chu.2020,
 author = {Chu, Casey and Minami, Kentaro and Fukumizu, Kenji},
 title = {{Smoothness and Stability in GANs}},
 url = {https://arxiv.org/pdf/2002.04185},
 booktitle = {{8th International Conference on Learning Representations (ICLR)}},
 year = {2020},
 abstract = {Generative adversarial networks, or GANs, commonly display unstable behavior during training. In this work, we develop a principled theoretical framework for understanding the stability of various types of GANs. In particular, we derive conditions that guarantee eventual stationarity of the generator when it is trained with gradient descent, conditions that must be satisfied by the divergence that is minimized by the GAN and the generator's architecture. We find that existing GAN variants satisfy some, but not all, of these conditions. Using tools from convex analysis, optimal transport, and reproducing kernels, we construct a GAN that fulfills these conditions simultaneously. In the process, we explain and clarify the need for various existing GAN stabilization techniques, including Lipschitz constraints, gradient penalties, and smooth activation functions.}
}


@inproceedings{Chu.2017,
 author = {Chu, Casey and Zhmoginov, Andrey and Sandler, Mark},
 title = {{CycleGAN, a Master of Steganography}},
 publisher = {{Curran Associates Inc}},
 series = {{NIPS'17}},
 editor = {von Luxburg, Ulrike and Guyon, Isabelle},
 booktitle = {{Advances in Neural Information Processing Systems 31 (NIPS 2017)}},
 year = {2017}
}


@inproceedings{Kodali.2018,
 author = {Kodali, Naveen and Abernethy, Jacob and Hays, James and Kira, Zsolt},
 title = {{On Convergence and Stability of GANs}},
 url = {https://arxiv.org/pdf/1705.07215},
 booktitle = {{6th International Conference on Learning Representations (ICLR)}},
 year = {2018},
 abstract = {We propose studying GAN training dynamics as regret minimization, which is in contrast to the popular view that there is consistent minimization of a divergence between real and generated distributions. We analyze the convergence of GAN training from this new point of view to understand why mode collapse happens. We hypothesize the existence of undesirable local equilibria in this non-convex game to be responsible for mode collapse. We observe that these local equilibria often exhibit sharp gradients of the discriminator function around some real data points. We demonstrate that these degenerate local equilibria can be avoided with a gradient penalty scheme called DRAGAN. We show that DRAGAN enables faster training, achieves improved stability with fewer mode collapses, and leads to generator networks with better modeling performance across a variety of architectures and objective functions.}
}


@book{Goodfellow.2016,
 author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
 year = {2016},
 title = {{Deep learning}},
 address = {Cambridge, Massachusetts and London, England},
 publisher = {{MIT Press}},
 isbn = {9780262035613},
 series = {{Adaptive computation and machine learning}},
 institution = {{MIT Press}},
 abstract = {Applied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models}
}


@inproceedings{Goodfellow.2014,
 author = {Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
 title = {{Generative Adversarial Nets}},
 url = {http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf},
 pages = {2672--2680},
 series = {{NIPS'14}},
 editor = {{Z. Ghahramani} and {M. Welling} and {C. Cortes} and {N. D. Lawrence} and {K. Q. Weinberger}},
 booktitle = {{Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS14)}},
 year = {2014}
}

@INPROCEEDINGS{Selvaraju.2017,
  author={Selvaraju, Ramprasaath R. and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
  booktitle={2017 IEEE International Conference on Computer Vision (ICCV)}, 
  title={Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization}, 
  year={2017},
  volume={},
  number={},
  pages={618-626},
  doi={10.1109/ICCV.2017.74}}


@InProceedings{Luu.2022,
title="Extending nn-UNet for Brain Tumor Segmentation",
author={Luu, Huan Minh and Park, Sung-Hong},
booktitle="Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="173--186",
}

@inproceedings{ChenSIMCLR.2020,
author = {Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
title = {A Simple Framework for Contrastive Learning of Visual Representations},
year = {2020},
publisher = {JMLR.org},
abstract = {This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by Sim-CLR achieves 76.5\% top-1 accuracy, which is a 7\% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1\% of the labels, we achieve 85.8\% top-5 accuracy, outperforming AlexNet with 100\texttimes{} fewer labels.},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
articleno = {149},
numpages = {11},
series = {ICML'20}
}
@inproceedings{Lugmayr.2022,
  title={Repaint: Inpainting using denoising diffusion probabilistic models},
  author={Lugmayr, Andreas and Danelljan, Martin and Romero, Andres and Yu, Fisher and Timofte, Radu and Van Gool, Luc},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11461--11471},
  year={2022}
}


@InProceedings{Kumar.2023,
author="Kumar, Komal
and Chakraborty, Snehashis
and Roy, Sudipta",
editor="Maji, Pradipta
and Huang, Tingwen
and Pal, Nikhil R.
and Chaudhury, Santanu
and De, Rajat K.",
title="Self-supervised Diffusion Model for Anomaly Segmentation in Medical Imaging",
booktitle="Pattern Recognition and Machine Intelligence",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="359--368",
abstract="A powerful mechanism for detecting anomalies in a self-supervised manner was demonstrated by model training on normal data, which can then be used as a baseline for scoring anomalies. Recent studies on diffusion models (DMs) have shown superiority over generative adversarial networks (GANs) and have achieved better-quality sampling over variational autoencoders (VAEs). Owing to the inherent complexity of the systems being modeled and the increased sampling times in the long sequence, DMs do not scale well to high-resolution imagery or a large amount of training data. Furthermore, in anomaly detection, DMs based on the Gaussian process do not control the target anomaly size and fail to repair the anomaly image, which led us to the development of a simplex diffusion and selective denoising {\$}{\$} ((SD)^2) {\$}{\$}((SD)2)model. {\$}{\$} (SD)^2 {\$}{\$}(SD)2does not require a full sequence of Markov chains in image reconstruction for anomaly detection, which reduces the time complexity, samples from the simplex noise diffusion process that have control over the anomaly size and are trained to reconstruct the selective features that help to repair the anomaly. {\$}{\$} (SD)^2 {\$}{\$}(SD)2significantly outperformed the publicly available Brats2021 and Phenomena detection from X-ray image datasets compared to the self-supervised model. The source code is made publicly available at https://github.com/MAXNORM8650/SDSquare.",
isbn="978-3-031-45170-6"
}


@inproceedings{Ristea.2022,
  title={Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection},
  author={Ristea, Nicolae-Catalin and Madan, Neelu and Ionescu, Radu Tudor and Nasrollahi, Kamal and Khan, Fahad Shahbaz and Moeslund, Thomas B and Shah, Mubarak},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2022}
}

@article{Suzuki.2017,
  title={Overview of deep learning in medical imaging},
  author={Suzuki, Kenji},
  journal={Radiological physics and technology},
  volume={10},
  number={3},
  pages={257--273},
  year={2017},
  publisher={Springer}
}

@article{Shen.2017,
  title={Deep learning in medical image analysis},
  author={Shen, Dinggang and Wu, Guorong and Suk, Heung-Il},
  journal={Annual review of biomedical engineering},
  volume={19},
  pages={221--248},
  year={2017},
  publisher={Annual Reviews}
}

@article{Kim.2019,
  title={Deep learning in medical imaging},
  author={Kim, Mingyu and Yun, Jihye and Cho, Yongwon and Shin, Keewon and Jang, Ryoungwoo and Bae, Hyun-jin and Kim, Namkug},
  journal={Neurospine},
  volume={16},
  number={4},
  pages={657},
  year={2019},
  publisher={Korean Spinal Neurosurgery Society}
}

@article{Chalapathy.2019,
  title={Deep learning for anomaly detection: A survey},
  author={Chalapathy, Raghavendra and Chawla, Sanjay},
  journal={arXiv preprint arXiv:1901.03407}     [Titel anhand dieser ArXiv-ID in Citavi-Projekt übernehmen] ,
  year={2019}
}

@article{Fernando.2021,
  title={Deep learning for medical anomaly detection--a survey},
  author={Fernando, Tharindu and Gammulle, Harshala and Denman, Simon and Sridharan, Sridha and Fookes, Clinton},
  journal={ACM Computing Surveys (CSUR)},
  volume={54},
  number={7},
  pages={1--37},
  year={2021},
  publisher={ACM New York, NY, USA}
}

@inproceedings{Tschuchnig.2022,
  title={Anomaly detection in medical imaging-a mini review},
  author={Tschuchnig, Maximilian E and Gadermayr, Michael},
  booktitle={Data Science--Analytics and Applications: Proceedings of the 4th International Data Science Conference--iDSC2021},
  pages={33--38},
  year={2022},
  organization={Springer}
}

@article{Behrendt.2023c,
  title={Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs},
  author={Behrendt, Finn and Bhattacharya, Debayan and Mieling, Robin and Maack, Lennart and Kr{\"u}ger, Julia and Opfer, Roland and Schlaefer, Alexander},
  journal={arXiv preprint arXiv:2312.04215},
  year={2023}
}

@inproceedings{Liu.2022,
  title={Multimodal Brain Tumor Segmentation Using Contrastive Learning Based Feature Comparison with Monomodal Normal Brain Images},
  author={Liu, Huabing and Nie, Dong and Shen, Dinggang and Wang, Jinda and Tang, Zhenyu},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={118--127},
  year={2022},
  organization={Springer}
}

@article{Bercea.2023d,
  title={Evaluating Normative Learning in Generative AI for Robust Medical Anomaly Detection},
  author={Bercea, Cosmin and Wiestler, Benedikt and Rueckert, Daniel and Schnabel, Julia},
  year={2023}
}

@article{Meissen.2022d,
  title={Unsupervised Anomaly Localization with Structural Feature-Autoencoders},
  author={Meissen, Felix and Paetzold, Johannes and Kaissis, Georgios and Rueckert, Daniel},
  journal={arXiv preprint arXiv:2208.10992},
  year={2022}
}

@InProceedings{Deng.2022,
author    = {Deng, Hanqiu and Li, Xingyu},
title     = {Anomaly Detection via Reverse Distillation From One-Class Embedding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month     = {June},
year      = {2022},
pages     = {9737-9746}}

@ARTICLE{Wang.2004,
  author={Zhou Wang and Bovik, A.C. and Sheikh, H.R. and Simoncelli, E.P.},
  journal={IEEE Transactions on Image Processing}, 
  title={Image quality assessment: from error visibility to structural similarity}, 
  year={2004},
  volume={13},
  number={4},
  pages={600-612},
  doi={10.1109/TIP.2003.819861}}


@article{PEREZGARCIA2021106236,
title = {TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning},
journal = {Computer Methods and Programs in Biomedicine},
volume = {208},
pages = {106236},
year = {2021},
issn = {0169-2607},
author = {Fernando Pérez-García and Rachel Sparks and Sébastien Ourselin},
keywords = {Medical image computing, Deep learning, Data augmentation, Preprocessing},

}


@article{Perlin.1985,
  title={An image synthesizer},
  author={Perlin, Ken},
  journal={ACM Siggraph Computer Graphics},
  volume={19},
  number={3},
  pages={287--296},
  year={1985},
  publisher={ACM New York, NY, USA}
}

