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MOOD 2020: a public benchmark for out-of-distribution detection and localization on medical images

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 prediction...

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Main Authors: Zimmerer, David (Author) , Full, Peter M. (Author) , Isensee, Fabian (Author) , Jaeger, Paul F. (Author) , Adler, Tim (Author) , Petersen, Jens (Author) , Köhler, Gregor (Author) , Roß, Tobias (Author) , Reinke, Annika (Author) , Kascenas, Antanas (Author) , Jensen, Bjørn Sand (Author) , O’Neil, Alison Q. (Author) , Tan, Jeremy (Author) , Hou, Benjamin (Author) , Batten, James (Author) , Qiu, Huaqi (Author) , Kainz, Bernhard (Author) , Shvetsova, Nina (Author) , Fedulova, Irina (Author) , Dylov, Dmitry V. (Author) , Yu, Baolun (Author) , Zhai, Jianyang (Author) , Hu, Jingtao (Author) , Si, Runxuan (Author) , Zhou, Sihang (Author) , Wang, Siqi (Author) , Li, Xinyang (Author) , Chen, Xuerun (Author) , Zhao, Yang (Author) , Marimont, Sergio Naval (Author) , Tarroni, Giacomo (Author) , Saase, Victor (Author) , Maier-Hein, Lena (Author) , Maier-Hein, Klaus H. (Author)
Format: Article (Journal)
Language:English
Published: [October 2022]
In: IEEE transactions on medical imaging
Year: 2022, Volume: 41, Issue: 10, Pages: 2728-2738
ISSN:1558-254X
DOI:10.1109/TMI.2022.3170077
Online Access:Verlag, Volltext: http://dx.doi.org/10.1109/TMI.2022.3170077
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Author Notes:David Zimmerer, Peter M. Full, Fabian Isensee, Paul Jäger, Tim Adler, Jens Petersen, Gregor Köhler, Tobias Ross, Annika Reinke, Antanas Kascenas, Bjørn Sand Jensen, Alison Q. O’Neil, Jeremy Tan, Benjamin Hou, James Batten, Huaqi Qiu, Bernhard Kainz, Nina Shvetsova, Irina Fedulova, Dmitry V. Dylov, Baolun Yu, Jianyang Zhai, Jingtao Hu, Runxuan Si, Sihang Zhou, Siqi Wang, Xinyang Li, Xuerun Chen, Yang Zhao, Sergio Naval Marimont, Giacomo Tarroni, Victor Saase, Lena Maier-Hein, and Klaus Maier-Hein
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Summary: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.
Item Description:Date of current version 30 September 2022
Gesehen am 18.01.2023
Physical Description:Online Resource
ISSN:1558-254X
DOI:10.1109/TMI.2022.3170077

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