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Unsupervised-Anomaly-Detection-in-Brain-MRI

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.

Citation

@article{TBD
}

Contents

Open Source Datasets

  • 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]

Papers

Feature based

  • 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]

Reconstruction based

  • AE

    • 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]

  • VAE

    • 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]

  • GAN

    • 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]

  • DM

    • 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]

  • Others

    • 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]

Synthetic Anomalies

  • 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]

Acknowledgements

We borrow the structure of this repository from this awesome repository