In this repository, we provide a collection of peer reviewed literature about Unsupervised Anomaly Detection (UAD) in brain MRI. We will try to keep the repository up-to-date and welcome contributions of others when a new matching paper is published or has completed peer-review.
@article{TBD
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Individual Brain Charting (IBC) dataset
Individual Brain Charting dataset extension, second release of high-resolution fMRI data for cognitive mapping
Pinho, Ana Lu'isa, Amadon, Alexis, Gauthier, Baptiste, Clairis, Nicolas, Knops, Andr'e, Genon, Sarah, Dohmatob, Elvis, Torre, Juan Jes'us, Ginisty, Chantal, Becuwe-Desmidt, S'everine, Roger, S'everine, Lecomte, Yann, Berland, Val'erie, Laurier, Laurence, Joly-Testault, V'eronique, M'ediouni-Cloarec, Ga"elle, Doubl'e
[2020] [Scientific data, 2020]
[Paper] [Access Data] -
Calgary-Campinas-359 (CC359) dataset
An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement
Souza, Roberto, Lucena, Oeslle, Garrafa, Julia, Gobbi, David, Saluzzi, Marina, Appenzeller, Simone, Rittner, Let'icia, Frayne, Richard, Lotufo, Roberto
[2018] [NeuroImage, 2018]
[Paper] [Access Data] -
Parkinson Progression Marker Initiative (PPMI) dataset
The Parkinson Progression Marker Initiative (PPMI)
[2011] [Progress in neurobiology, 2011]
[Paper] [Access Data] -
Developing Human Connectome Project (dHCP) dataset
A dedicated neonatal brain imaging system
Hughes, Emer J., Winchman, Tobias, Padormo, Francesco, Teixeira, Rui, Wurie, Julia, Sharma, Maryanne, Fox, Matthew, Hutter, Jana, Cordero-Grande, Lucilio, Price, Anthony N., Allsop, Joanna, Bueno-Conde, Jose, Tusor, Nora, Arichi, Tomoki, Edwards, A. D., Rutherford, Mary A., Counsell, Serena J., Hajnal, Joseph V.
[2017] [Magnetic resonance in medicine, 2017]
[Paper] [Access Data] -
Information eXtraction from Images (IXI) dataset
Biomedical Image Analysis Group
[Access Data] -
The Human Connectome Project (HPC) dataset
The Human Connectome Project: a data acquisition perspective
van Essen
[2012] [NeuroImage, 2012]
[Paper] [Access Data] -
The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset
The Alzheimer's Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement
Weiner, Michael W., Veitch, Dallas P., Aisen, Paul S., Beckett, Laurel A., Cairns, Nigel J., Green, Robert C., Harvey, Danielle, Jack, Clifford R., Jagust, William, Morris, John C., Petersen, Ronald C., Salazar, Jennifer, Saykin, Andrew J., Shaw, Leslie M., Toga, Arthur W., Trojanowski, John Q.
[2017] [Alzheimer's, 2017]
[Paper] [Access Data] -
The Open Access Series of Imaging Studies (OASIS) dataset
OASIS-3: Longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer's disease
LaMontagne, Pamela J., Keefe, Sarah, Lauren, Wallace, Xiong, Chengjie, Grant, Elizabeth A., Moulder, Krista L., Morris, John C., Benzinger, Tammie L.S., Marcus, Daniel S.
[2018] [Alzheimer's, 2018]
[Paper] [Access Data] -
The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) dataset
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
Taylor, Jason R., Williams, Nitin, Cusack, Rhodri, Auer, Tibor, Shafto, Meredith A., Dixon, Marie, Tyler, Lorraine K., Cam-Can, Henson, Richard N.
[2017] [NeuroImage, 2017]
[Paper] [Access Data] -
UK biobank (UKB) dataset
UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age
Sudlow, Cathie, Gallacher, John, Allen, Naomi, Beral, Valerie, Burton, Paul, Danesh, John, Downey, Paul, Elliott, Paul, Green, Jane, Landray, Martin, Liu, Bette, Matthews, Paul, Ong, Giok, Pell, Jill, Silman, Alan, Young, Alan, Sprosen, Tim, Peakman, Tim, Collins, Rory
[2015] [PLoS medicine, 2015]
[Paper] [Access Data] -
fastMRI (fMRI) dataset
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Zbontar, Jure, Knoll, Florian, Sriram, Anuroop, Murrell, Tullie, Huang, Zhengnan, Muckley, Matthew J., Defazio, Aaron, Stern, Ruben, Johnson, Patricia, Bruno, Mary, Parente, Marc, Geras, Krzysztof J., Katsnelson, Joe, Chandarana, Hersh, Zhang, Zizhao, Drozdzal, Michal, Romero, Adriana, Rabbat, Michael, Vincent, Pascal, Yakubova, Nafissa, Pinkerton, James, Wang, Duo, Owens, Erich, Zitnick, C. Lawrence, Recht, Michael P., Sodickson, Daniel K., Lui, Yvonne W.
[2019] [ArXiv]
[Paper] [Access Data] -
The Maryland Magnets Prospective (MagNets) dataset
Investigation of Prognostic Ability of Novel Imaging Markers for Traumatic Brain Injury (TBI)
Gullapalli, Rao P.
[2011][Defense Technical Information Center]
[Report] [Access Data] -
The Multiple Sclerosis data set from the University Hospital of Ljubljana (MSLUB) dataset
A novel public MR image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus
Lesjak, \vZiga, Galimzianova, Alfiia, Koren, Ale\vs, Lukin, Matej, Pernu\vs
[2018] [Neuroinformatics, 2018]
[Paper] [Access Data] -
The Multiple Sclerosis Segmentation Challenge (MSSEG) dataset
Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
Commowick, Olivier, Istace, Audrey, Kain, Micha"el, Laurent, Baptiste, Leray, Florent, Simon, Mathieu, Pop, Sorina Camarasu, Girard, Pascal, Am'eli, Roxana, Ferr'e
[2018] [Scientific Reports, 2018]
[Paper] [Access Data] -
The Anatomical Tracings of Lesions After Stroke (ATLAS) data set (V1, V2)
A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms
Liew, Sook-Lei, Lo, Bethany P., Donnelly, Miranda R., Zavaliangos-Petropulu, Artemis, Jeong, Jessica N., Barisano, Giuseppe, Hutton, Alexandre, Simon, Julia P., Juliano, Julia M., Suri, Anisha, others
[2022] [Scientific data, 2022]
[Paper] [Access Data] -
The white matter hyperintensities (WMH) dataset
Standardized assessment of automatic segmentation of white matter hyperintensities and results of the WMH segmentation challenge
Kuijf, Hugo J., Biesbroek, J. Matthijs, de Bresser, Jeroen, Heinen, Rutger, Andermatt, Simon, Bento, Mariana, Berseth, Matt, Belyaev, Mikhail, Cardoso, M. Jorge, Casamitjana, Adria, others
[2019] [IEEE transactions on medical imaging, 2019]
[Paper] [Access Data] -
The ischemic stroke lesion segmentation (ISLES) dataset
ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI
Maier, Oskar, Menze, Bjoern H., von der Gablentz, Janina, Hani, Levin, Heinrich, Mattias P., Liebrand, Matthias, Winzeck, Stefan, Basit, Abdul, Bentley, Paul, Chen, Liang, Christiaens, Daan, Dutil, Francis, Egger, Karl, Feng, Chaolu, Glocker, Ben, Goetz, Michael, Haeck, Tom, Halme, Hanna-Leena, Havaei, Mohammad, Iftekharuddin, Khan M., Jodoin, Pierre-Marc, Kamnitsas, Konstantinos, Kellner, Elias, Korvenoja, Antti, Larochelle, Hugo, Ledig, Christian, Lee, Jia-Hong, Maes, Frederik, Mahmood, Qaiser, Maier-Hein, Klaus H., McKinley, Richard, Muschelli, John, Pal, Chris, Pei, Linmin, Rangarajan, Janaki Raman, Reza, Syed M. S., Robben, David, Rueckert, Daniel, Salli, Eero, Suetens, Paul, Wang, Ching-Wei, Wilms, Matthias, Kirschke, Jan S., Kr Amer
[2017] [Medical Image Analysis, 2017]
[Paper] [Access Data] -
The centre for clinical brain sciences (CBS) dataset
A structural and functional magnetic resonance imaging dataset of brain tumour patients
Pernet, Cyril R., Gorgolewski, Krzysztof J., Job, Dominic, Rodriguez, David, Whittle, Ian, Wardlaw, Joanna
[2016] [Scientific data, 2016]
[Paper] Access Data] -
The Multimodal Brain Tumor Segmentation (BraTS) datasets (2021 version)
The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification
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
[2021][ArXiv]
[Paper] Access Data]
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Modeling normal brain asymmetry in MR images applied to anomaly detection without segmentation and data annotation
Martins, Samuel, Barbara Caroline Benato, Bruna Ferreira Silva, Clarissa Lyn Yasuda, Alexandre Xavier Falc~ao
[2019] [SPIE]
[Paper] -
Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening
Alaverdyan, Zaruhi, Jung, Julien, Bouet, Romain, Lartizien, Carole
[2020] [Medical Image Analysis, 2020]
[Paper] -
SS3D: Unsupervised Out-of-Distribution Detection and Localization for Medical Volumes
Doorenbos, Lars, Sznitman, Raphael, M'arquez-Neila, Pablo
[2021][MICCAI - Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis]
[Paper] [Code] -
Anomaly Detection via Context and Local Feature Matching
Kascenas, Antanas, Young, Rory, Jensen, Bjorn Sand, Pugeault, Nicolas, O'Neil, Alison Q.
[2022] [2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022]
[Paper] -
Unsupervised anomaly localization with structural feature-autoencoders
Meissen, Felix, Paetzold, Johannes, Kaissis, Georgios, Rueckert, Daniel
[2022][MICCAI - Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries]
[Paper] [Code] -
Brain Subtle Anomaly Detection Based on Auto-Encoders Latent Space Analysis: Application To De Novo Parkinson Patients
Pinon, Nicolas, Oudoumanessah, Geoffroy, Trombetta, Robin, Dojat, Michel, Forbes, Florence, Lartizien, Carole
[2023] [2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023]
[Paper] -
One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities
Pinon, Nicolas, Trombetta, Robin, Lartizien Carole*
[2023] [Medical Imaging with Deep Learning, 2023]
[Paper] -
Feature-Based Pipeline for Improving Unsupervised Anomaly Segmentation on Medical Images
Frolova, Daria, Katrutsa, Aleksandr, Oseledets, Ivan
[2023] [MICCAI - Uncertainty for Safe Utilization of Machine Learning in Medical Imaging]
[Paper] [Code] -
Encoder-Decoder Contrast for Unsupervised Anomaly Detection in Medical Images
Guo, Jia, Lu, Shuai, Jia, Lize, Zhang, Weihang, Li, Huiqi
[2023] [IEEE transactions on medical imaging, 2023]
[Paper] [Code] -
Contrastive Representations for Unsupervised Anomaly Detection and Localization
Lüth, Carsten, Zimmerer, David, Koehler, Gregor, Jaeger, Paul, Isensee, Fabian, Maier-Hein, Klaus
[2023] [BVM, 2023]
[Paper]
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AE
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Bayesian Skip-Autoencoders for Unsupervised Hyperintense Anomaly Detection in High Resolution Brain Mri
Baur, Christoph, Wiestler, Benedikt, Albarqouni, Shadi, Navab, Nassir
[2020] [2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020]
[Paper] -
Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI
Baur, Christoph, Wiestler, Benedikt, Albarqouni, Shadi, Navab, Nassir
[2020] [Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020, 2020]
[Paper] -
Modeling Healthy Anatomy with Artificial Intelligence for Unsupervised Anomaly Detection in Brain MRI
Baur, Christoph, Wiestler, Benedikt, Muehlau, Mark, Zimmer, Claus, Navab, Nassir, Albarqouni, Shadi
[2021] [Radiology: Artificial Intelligence, 2021]
[Paper] -
Denoising Autoencoders for Unsupervised Anomaly Detection in Brain MRI
Kascenas, Antanas, Pugeault, Nicolas, O'Neil, Alison Q.
[2022] [Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, 2022]
[Paper] [Code] -
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders
Chen, Xiaoran, Konukoglu, Ender
[2022] [Medical Imaging with Deep Learning, 2022]
[Paper] [Code] -
Federated disentangled representation learning for unsupervised brain anomaly detection
Bercea, Cosmin I., Wiestler, Benedikt, Rueckert, Daniel, Albarqouni, Shadi
[2022] [Nature Machine Intelligence, 2022]
[Paper] [Code] -
A dual autoencoder and singular value decomposition based feature optimization for the segmentation of brain tumor from MRI images
Aswani, K., Menaka, D.
[2021] [BMC Medical Imaging, 2021]
[Paper] -
Unsupervised Anomaly Detection in 3D Brain MRI Using Deep Learning with Impured Training Data
Behrendt, Finn, Bengs, Marcel, Rogge, Frederik, Kruger, Julia, Opfer, Roland, Schlaefer, Alexander
[2022] [2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022]
[Paper] -
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images
Cai, Yu, Chen, Hao, Yang, Xin, Zhou, Yu, Cheng, Kwang-Ting
[2023] [Medical Image Analysis, 2023]
[Paper] [Code] -
Outlier detection in multimodal MRI identifies rare individual phenotypes among more than 15,000 brains
Ma, Zhiwei, Reich, Daniel S., Dembling, Sarah, Duyn, Jeff H., Koretsky, Alan P.
[2022] [Human brain mapping, 2022]
[Paper] -
Subtle anomaly detection: Application to brain MRI analysis of de novo Parkinsonian patients
Mu~noz-Ram'irez, Ver'onica, Kmetzsch, Virgilio, Forbes, Florence, Meoni, Sara, Moro, Elena, Dojat, Michel
[2022] [Artificial Intelligence in Medicine, 2022]
[Paper] -
Unsupervised anomaly detection in brain MRI: Learning abstract distribution from massive healthy brains
Luo, Guoting, Xie, Wei, Gao, Ronghui, Zheng, Tao, Chen, Lei, Sun, Huaiqiang
[2023] [Computers in Biology and Medicine, 2023]
[Paper]
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VAE
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Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
Zimmerer, David, Kohl, Simon, Petersen, Jens, Isensee, Fabian, Maier-Hein, Klaus
[2019] [International Conference on Medical Imaging with Deep Learning (MIDL), 2019]
[Paper] -
Unsupervised Anomaly Localization Using Variational Auto-Encoders
Zimmerer, David, Isensee, Fabian, Petersen, Jens, Kohl, Simon, Maier-Hein, Klaus
[2019] [Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019, 2019]
[Paper] [Code] -
Predictable Uncertainty-Aware Unsupervised Deep Anomaly Segmentation
Sato, Kazuki, Hama, Kenta, Matsubara, Takashi, Uehara, Kuniaki
[2019] [2019 International Joint Conference on Neural Networks (IJCNN 2019), 2019]
[Paper] -
Unsupervised lesion detection via image restoration with a normative prior
Chen, Xiaoran, You, Suhang, Tezcan, Kerem Can, Konukoglu, Ender
[2020] [Medical Image Analysis, 2020]
[Paper] [Code] -
Tumor Detection in Brain MRIs by Computing Dissimilarities in the Latent Space of a Variational AutoEncoder
Albu, Alexandra, Enescu, Alina, Malag`o
[2020] [Proceedings of the Northern Lights Deep Learning Workshop, 2020]
[Paper] -
Brain Lesion Detection Using A Robust Variational Autoencoder and Transfer Learning
Akrami, Haleh, Joshi, Anand A., Li, Jian, Aydore, Sergul, Leahy, Richard M.
[2020] [IEEE 17th International Symposium on Biomedical Imaging, 2020]
[Paper] -
Unsupervised pathology detection in medical images using conditional variational autoencoders
Uzunova, Hristina, Schultz, Sandra, Handels, Heinz, Ehrhardt, Jan
[2019] [International journal of computer assisted radiology and surgery, 2019]
[Paper] -
Leveraging 3d Information In Unsupervised Brain Mri Segmentation
Lambert, Benjamin, Louis, Maxime, Doyle, Senan, Forbes, Florence, Dojat, Michel, Tucholka, Alan
[2021] [2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021]
[Paper] -
Constrained unsupervised anomaly segmentation
Silva-Rodr'iguez, Julio, Naranjo, Valery, Dolz, Jose
[2022] [Medical Image Analysis, 2022]
[Paper] [Code] -
StRegA: Unsupervised anomaly detection in brain MRIs using a compact context-encoding variational autoencoder
Chatterjee, Soumick, Sciarra, Alessandro, D"unnwald, Max, Tummala, Pavan, Agrawal, Shubham Kumar, Jauhari, Aishwarya, Kalra, Aman, Oeltze-Jafra, Steffen, Speck, Oliver, N"urnberger, Andreas
[2022] [Computers in Biology and Medicine, 2022]
[Paper] [Code] -
The OOD Blind Spot of Unsupervised Anomaly Detection
Matth"aus Heer, Janis Postels, Xiaoran Chen, Ender Konukoglu, Shadi Albarqouni
[2021] [Medical Imaging with Deep Learning, 2021]
[Paper] -
Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening
Bercea, Cosmin, Benedikt Wiestler, Daniel Rueckert, Julia A Schnabel
[2023] [Medical Imaging with Deep Learning, 2023]
[Paper] [Code] -
Three-dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain MRI
Bengs, Marcel, Behrendt, Finn, Kr"uger, Julia, Opfer, Roland, Schlaefer, Alexander
[2021] [International journal of computer assisted radiology and surgery, 2021]
[Paper] -
Unsupervised anomaly detection in 3D brain MRI using deep learning with multi-task brain age prediction
Marcel Bengs, Finn Behrendt, Max-Heinrich Laves, Julia Kr"uger, Roland Opfer, Alexander Schlaefer
[2022] [Medical Imaging 2022: Computer-Aided Diagnosis, 2022]
[Paper] -
Capturing Inter-Slice Dependencies of 3D Brain MRI-Scans for Unsupervised Anomaly Detection
Finn Behrendt, Marcel Bengs, Debayan Bhattacharya, Julia Kr"uger, Roland Opfer, Alexander Schlaefer
[2022] [Medical Imaging with Deep Learning, 2022]
[Paper] -
On the Pitfalls of Using the Residual Error as Anomaly Score
Meissen, Felix, Wiestler, Benedikt, Kaissis, Georgios, Rueckert, Daniel
[Paper] [Code] -
Unsupervised Brain Anomaly Detection and Segmentation with Transformers
Pinaya, Walter Hugo Lopez, Tudosiu, Petru-Daniel, Gray, Robert, Rees, Geraint, Nachev, Parashkev, Ourselin, S'ebastien, Cardoso, M. Jorge
[2021] [Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, 2021]
[Paper] [Code] -
Unsupervised abnormality detection in neonatal MRI brain scans using deep learning
Raad, Jad Dino, Chinnam, Ratna Babu, Arslanturk, Suzan, Tan, Sidhartha, Jeong, Jeong-Won, Mody, Swati
[2023] [Scientific reports, 2023]
[Paper] [Code]
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GAN
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Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
Baur, Christoph, Wiestler, Benedikt, Albarqouni, Shadi, Navab, Nassir
[2019] [Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2019]
[Paper] -
SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI
Baur, Christoph, Graf, Robert, Wiestler, Benedikt, Albarqouni, Shadi, Navab, Nassir
[2020] [Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020, 2020]
[Paper] -
Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection
Bercea, Cosmin I., Wiestler, Benedikt, Rueckert, Daniel, Schnabel, Julia A.
[2020] [Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020]
[Paper] [Code] -
MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction
Han, Changhee, Rundo, Leonardo, Murao, Kohei, Noguchi, Tomoyuki, Shimahara, Yuki, Milacski, Zolt'an 'Ad'am, Koshino, Saori, Sala, Evis, Nakayama, Hideki, Satoh, Shin'ichi
[2021] [BMC bioinformatics, 2021]
[Paper] -
Unsupervised Region-Based Anomaly Detection In Brain MRI With Adversarial Image Inpainting
Nguyen, Bao, Feldman, Adam, Bethapudi, Sarath, Jennings, Andrew, Willcocks, Chris G.
[2021] [2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021]
[Paper] -
An anomaly detection approach to identify chronic brain infarcts on MRI
van Hespen
[2021] [Scientific Reports, 2021]
[Paper]
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DM
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Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
Pinaya, Walter H. L., Graham, Mark S., Gray, Robert, Da Costa
[2022] [Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022]
[Paper] [Code] -
Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise
Wyatt, Julian, Leach, Adam, Schmon, Sebastian M., Willcocks, Chris G.
[Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, ]
[Paper] [Code] -
Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI
Finn Behrendt, Debayan Bhattacharya, Julia Kr"uger, Roland Opfer, Alexander Schlaefer
[2023] [Medical Imaging with Deep Learning, 2023]
[Paper] [Code] -
The role of noise in denoising models for anomaly detection in medical images
Kascenas, Antanas, Sanchez, Pedro, Schrempf, Patrick, Wang, Chaoyang, Clackett, William, Mikhael, Shadia S., Voisey, Jeremy P., Goatman, Keith, Weir, Alexander, Pugeault, Nicolas, Tsaftaris, Sotirios A., O'Neil, Alison Q.
[2023] [Medical Image Analysis, 2023]
[Paper] -
Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models
Bercea, Cosmin, Michael Neumayr, Daniel Rueckert, Julia A Schnabel
[2023] [ICML 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH), 2023]
[Paper] [Code] -
Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model
Iqbal, Hasan, Khalid, Umar, Chen, Chen, Hua, Jing
[2023] [MICCAI - Machine Learning in Medical Imaging, 2023]
[Paper] [Code] -
Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI
Liang, Ziyun, Anthony, Harry, Wagner, Felix, Kamnitsas, Konstantinos
[2023] [MICCAI Workshop]
[Paper] [Code] -
Self-supervised diffusion model for anomaly segmentation in medical imaging
*Kumar, Komal, Chakraborty, Snehashis, Roy, Sudipta *
[2023] [International Conference on Pattern Recognition and Machine Intelligence]
[Paper] [Code]
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Others
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Implicit Field Learning for Unsupervised Anomaly Detection in Medical Images
Marimont, Naval , Tarroni, Giacomo
[2021][Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021]
[Paper] [Code] -
Challenging Current Semi-supervised Anomaly Segmentation Methods for~Brain MRI
Meissen, Felix, Kaissis, Georgios, Rueckert, Daniel
[2022] [Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2022]
[Paper] [Code] -
Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study
Baur, Christoph, Stefan Denner, Benedikt Wiestler, Nassir Navab, Shadi Albarqouni
[2021] [Medical Image Analysis, 2021]
[Paper] [Code] -
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art
Lagogiannis, Ioannis, Meissen, Felix, Kaissis, Georgios, Rueckert, Daniel
[2023] [IEEE transactions on medical imaging, 2023]
[Paper] [Code]
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SS3D: Unsupervised Out-of-Distribution Detection and Localization for Medical Volumes
Doorenbos, Lars, Sznitman, Raphael, M'arquez-Neila, Pablo
[2021][MICCAI - Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis]
[Paper] [Code] -
AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation
Meissen, Felix, Kaissis, Georgios, Rueckert, Daniel
[2021][MICCAI - Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis]
[Paper] [Code] -
Self-supervised 3D Out-of-Distribution Detection via Pseudoanomaly Generation
Cho, Jihoon, Kang, Inha, Park, Jinah
[2021][MICCAI - Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis]
[Paper] -
Self-supervised Medical Out-of-Distribution Using U-Net Vision Transformers
Park, Seongjin, Balint, Adam, Hwang, Hyejin
[2021][MICCAI - Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis]
[Paper] -
MetaDetector: Detecting Outliers by Learning to Learn from Self-supervision
Tan, Jeremy, Kart, Turkay, Hou, Benjamin, Batten, James, Kainz, Bernhard
[2021][MICCAI - Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis]
[Paper] -
Detecting Outliers with Foreign Patch Interpolation \
Tan, Jeremy and Hou, Benjamin and Batten, James and Qiu, Huaqi and Kainz, Bernhard
[2022][Machine Learning for Biomedical Imaging]
[Paper] [Code] -
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks \
Baugh, Matthew, Tan, Jeremy, Müller, Johanna, Dombrowski, Mischa, Batten, James, Kainz, Bernhard
[2022][MICCAI]
[Paper] [Code]
We borrow the structure of this repository from this awesome repository