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Lagogiannis, I.* ; Meissen, F.* ; Kaissis, G. ; Rueckert, D.*

Unsupervised pathology detection: A deep dive Into the state of the art.

IEEE Trans. Med. Imaging 43, 241-252 (2024)
DOI PMC
Deep unsupervised approaches are gathering increased attention for applications such as pathology detection and segmentation in medical images since they promise to alleviate the need for large labeled datasets and are more generalizable than their supervised counterparts in detecting any kind of rare pathology. As the Unsupervised Anomaly Detection (UAD) literature continuously grows and new paradigms emerge, it is vital to continuously evaluate and benchmark new methods in a common framework, in order to reassess the state-of-the-art (SOTA) and identify promising research directions. To this end, we evaluate a diverse selection of cutting-edge UAD methods on multiple medical datasets, comparing them against the established SOTA in UAD for brain MRI. Our experiments demonstrate that newly developed feature-modeling methods from the industrial and medical literature achieve increased performance compared to previous work and set the new SOTA in a variety of modalities and datasets. Additionally, we show that such methods are capable of benefiting from recently developed self-supervised pre-training algorithms, further increasing their performance. Finally, we perform a series of experiments in order to gain further insights into some unique characteristics of selected models and datasets. Our code can be found under https://github.com/iolag/UPD_study/.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Anomaly detection; Medical diagnostic imaging; Image reconstruction; Pathology; Feature extraction; Training; Task analysis; Unsupervised; anomaly; detection; segmentation; medical; comparative; generative; image-reconstruction; feature-modeling; self-supervised; pre-training; Anomaly Detection; Segmentation
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Journal IEEE Transactions on Medical Imaging
Quellenangaben Volume: 43, Issue: 1, Pages: 241-252 Article Number: , Supplement: ,
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Publishing Place New York, NY [u.a.]
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
Grants Munich Center for Machine Learning