MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images

IEEE Transactions on Medical Imaging - Tập 41 Số 10 - Trang 2728-2738 - 2022
David Zimmerer1, Peter M. Full1, Fabian Isensee1, Paul F. Jäger1, Tim Adler1, Jens Petersen1, Gregor Koehler1, Tobias Roß1, Annika Reinke1, Antanas Kascenas2, Bjørn Sand Jensen2, Alison Q. O’Neil3, Jeremy Tan4, Benjamin Hou4, James Batten4, Huaqi Qiu4, Bernhard Kainz4, Nina Shvetsova5, Irina Fedulova5, Dmitry V. Dylov6, Baolun Yu7, Jianyang Zhai7, Jingtao Hu7, Runxuan Si7, Sihang Zhou8, Siqi Wang7, Xinyang Li7, Xuerun Chen7, Yang Zhao7, Sergio Naval Marimont9, Giacomo Tarroni9, Victor Saase10, Lena Maier‐Hein1, Klaus H. Maier‐Hein1
1German Cancer Research Center, Heidelberg, Germany
2School of Computing Science, University of Glasgow, Glasgow, U.K.
3School of Engineering, The University of Edinburgh, Edinburgh, U.K.
4Department of Computing, Imperial College London, London, U.K
5Philips Research, Moscow, Russia
6Skolkovo Institute of Science and Technology, Moscow, Russia
7College of Computer, National University of Defense Technology, Changsha, Hunan, China
8College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
9CitAI Research Centre, City, University of London, London, U.K.
10Department of Neuroradiology, Heidelberg University, Heidelberg, Germany

Tóm tắt

Từ khóa


Tài liệu tham khảo

10.1377/hlthaff.27.6.1491

10.1001/jama.2019.11456

10.1148/radiol.11091710

10.2214/AJR.18.20392

Hendrycks, 2016, A baseline for detecting misclassified and out-of-distribution examples in neural networks, arXiv:1610.02136

Mehrtash, 2019, Confidence calibration and predictive uncertainty estimation for deep medical image segmentation, arXiv:1911.13273

Roady, 2019, Are out-of-distribution detection methods effective on large-scale datasets?, arXiv:1910.14034

Shafaei, 2019, A less biased evaluation of out-of-distribution sample detectors, arXiv:1809.04729

10.1177/0956797613479386

Abati, 2018, Latent space autoregression for novelty detection, arXiv:1807.01653

10.1609/aaai.v34i04.5712

10.1109/IJCNN.2019.8851808

10.1007/978-3-030-46150-8_13

Choi, 2018, WAIC, but why? Generative ensembles for robust anomaly detection, arXiv:1810.01392

Guggilam, 2019, Bayesian anomaly detection using extreme value theory, arXiv:1905.12150

Maaløe, 2019, BIVA: A very deep hierarchy of latent variables for generative modeling, arXiv:1902.02102

10.1007/978-3-030-30642-7_23

10.1109/CVPR.2018.00356

Chen, 2018, Deep generative models in the real-world: An open challenge from medical imaging, arXiv:1806.05452

10.1007/978-3-030-11723-8_16

10.1148/ryai.2021190169

10.1007/978-3-319-59050-9_12

10.1007/978-3-030-32251-9_32

Avati, 2021, BEDS-bench: Behavior of EHR-models under distributional shift—A benchmark, arXiv:2107.08189

Ulmer, Trust issues: Uncertainty estimation does not enable reliable ood detection on medical tabular data, Proc. Mach. Learn. Health, 341

10.1109/CVPR.2019.00982

Hendrycks, 2019, Scaling out-of-distribution detection for real-world settings, arXiv:1911.11132

Goldstein, 2014, Anomaly Detection in Large Datasets

Škvára, 2018, Are generative deep models for novelty detection truly better?, arXiv:1807.05027

10.1016/j.neuroimage.2012.02.018

10.1056/nejmoa0800996

Zimmerer, 2020, Medical out-of-distribution analysis challenge

Zimmerer, 2020, Synapse

Zimmerer, 2020, Website

Zimmerer, 2020, Sklearn

Bergmann, 2019, Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings, arXiv:1911.02357

10.1038/s41598-021-82017-6

Zimmerer, 2021, Github

10.59275/j.melba.2022-e651

10.1109/ACCESS.2021.3107163

Larsen, 2015, Autoencoding beyond pixels using a learned similarity metric, arXiv:1512.09300

Simonyan, 2014, Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556

Chen, 2020, A simple framework for contrastive learning of visual representations, arXiv:2002.05709

van den Oord, 2017, Neural discrete representation learning, arXiv:1711.00937

Chen, 2017, PixelSNAIL: An improved autoregressive generative model, arXiv:1712.09763

Marimont, 2020, Anomaly detection through latent space restoration using vector-quantized variational autoencoders, arXiv:2012.06765

Saase, 2020, Simple statistical methods for unsupervised brain anomaly detection on MRI are competitive to deep learning methods, arXiv:2011. 12735

10.1007/978-3-031-08999-2_5

Winkens, 2020, Contrastive training for improved out-of-distribution detection, arXiv:2007.05566

Meissen, On the pitfalls of using the residual as anomaly score, Proc. Med. Imag. With Deep Learn., 1

Isensee, 2018, NnU-Net: Self-adapting framework for U-Net-based medical image segmentation, arXiv:1809.10486

10.1109/CVPR46437.2021.00954

10.1007/978-3-030-87240-3_56

Kascenas, Denoising autoencoders for unsupervised anomaly detection in brain MRI, Proc. Med. Imag. With Deep Learn., 1

Pinaya, Unsupervised brain anomaly detection and segmentation with transformers, Proc. Med. Imag. With Deep Learn., 1