nnOOD: A framework for benchmarking self-supervised anomaly localisation methods
OA Location
Author(s)
Baugh, Matthew
Tan, Jeremy
Vlontzos, Athanasios
Mueller, Johanna P
Kainz, Bernhard
Type
Chapter
Abstract
The wide variety of in-distribution and out-of-distribution data in medical imaging makes universal anomaly detection a challenging task. Recently a number of self-supervised methods have been developed that train end-to-end models on healthy data augmented with synthetic anomalies. However, it is difficult to compare these methods as it is not clear whether gains in performance are from the task itself or the training pipeline around it. It is also difficult to assess whether a task generalises well for universal anomaly detection, as they are often only tested on a limited range of anomalies. To assist with this we have developed nnOOD, a framework that adapts nnU-Net to allow for comparison of self-supervised anomaly localisation methods. By isolating the synthetic, self-supervised task from the rest of the training process we perform a more faithful comparison of the tasks, whilst also making the workflow for evaluating over a given dataset quick and easy. Using this we have implemented the current state-of-the-art tasks and evaluated them on a challenging X-ray dataset.
Editor(s)
Sudre, CH
Baumgartner, CF
Dalca, A
Qin, C
Tanno, R
VanLeemput, K
Wells, WM
Date Issued
2022-09-14
Citation
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, 2022, 13563, pp.103-112
ISBN
978-3-031-16748-5
Publisher
Springer Nature Switzerland AG
Start Page
103
End Page
112
Journal / Book Title
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging
Lecture Notes in Computer Science
Volume
13563
Copyright Statement
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG.
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000877066500010&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
Anomaly localisation
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Life Sciences & Biomedicine
Radiology, Nuclear Medicine & Medical Imaging
Science & Technology
Self-supervised learning
Technology
Publication Status
Published
Date Publish Online
2022-09-14