The Effectiveness of Self-supervised Pre-training for Multi-modal Endometriosis Classification*†

David Butler, Hu Wang, Yuan Zhang, Minh-Son To, Minh‐Son To, George Condous, Mathew Leonardi, Steven Knox, Jodie Avery, M Louise Hull, Gustavo Carneiro
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023 · vol. 2023 , pp. 1–5 · doi:10.1109/embc40787.2023.10340504 · PMID:38083681 · W4389543260
article OA: closed CC0 ⤵ 1 in-corpus citation
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This paper evaluated self-supervised pre-training, including a novel 3D masking method, for multi-modal MRI endometriosis classification, demonstrating up to a 31% improvement over training from scratch.

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Abstract

Endometriosis is a debilitating condition affecting 5% to 10% of the women worldwide, where early detection and treatment are the best tools to manage the condition. Early detection can be done via surgery, but multi-modal medical imaging is preferable given the simpler and faster process. However, imaging-based endometriosis diagnosis is challenging as 1) there are few capable clinicians; and 2) it is characterised by small lesions unconfined to a specific location. These two issues challenge the development of endometriosis classifiers as the training datasets tend to be small and contain difficult samples, which leads to overfitting. Hence, it is important to consider generalisation techniques to mitigate this problem, particularly self-supervised pre-training methods that have shown outstanding results in computer vision and natural language processing applications. The main goal of this paper is to study the effectiveness of modern self-supervised pre-training techniques to overcome the two issues mentioned above for the classification of endometriosis from multi-modal imaging data. We also introduce a new masking image modelling self-supervised pre-training method that works with 3D multi-modal medical imaging. Furthermore, to the best of our knowledge, this paper presents the first endometriosis classifier, fine-tuned from the pre-trained model above, which works with multi-modal (i.e., T1 and T2) magnetic resonance imaging (MRI) data. Our results show that self-supervised pre-training improves endometriosis classification by as much as 31%, when compared with classifiers trained from scratch.

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Condition tags

endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Female Female Female Female Female Female Female Female Humans Humans Humans

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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