Unpaired multi-modal training and single-modal testing for detecting signs of endometriosis

Yuan Zhang, Hu Wang, Wang H, David Butler, Butler D, Smart B, Brandon Smart, Yutong Xie, Xie Y, Minh‐Son To, To MS, Minh-Son To, Knox S, Steven Knox, Condous G, George Condous, Mathew Leonardi, Avery JC, Jodie C Avery, Jodie Avery, Hull ML, M Louise Hull, Gustavo Carneiro, Carneiro G
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025 · vol. 124 , pp. 102575 · doi:10.1016/j.compmedimag.2025.102575 · PMID:40472422 · W4410856007
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This paper developed a method for training a deep learning model on combined ultrasound and MRI data, and then using that trained model to detect endometriosis using only one of the modalities.

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Abstract

Endometriosis is a serious multifocal condition that can involve various pelvic structures, with Pouch of Douglas (POD) obliteration being a significant clinical indicator for diagnosis. To circumvent the need for invasive diagnostic procedures like laparoscopy, research has increasingly focused on imaging-based methods such as transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI). The limited diagnostic accuracy achieved through manual interpretation of these imaging techniques has driven the development of automated classifiers that can effectively utilize both modalities. However, patients often undergo only one of these two examinations, resulting in unpaired data for training and testing POD obliteration classifiers, where TVUS models tend to be more accurate than MRI models, but TVUS scanning are more operator dependent. This prompts a crucial question: Can a model be trained with unpaired TVUS and MRI data to enhance the performance of a model exclusively trained with MRI, while maintaining the high accuracy of the model individually trained with TVUS? In this paper we aim to answer this question by proposing a novel multi-modal POD obliteration classifier that is trained with unpaired TVUS and MRI data and tested using either MRI or TVUS data. Our method is the first POD obliteration classifier that can flexibly take either the TVUS or MRI data, where the model automatically focuses on the uterus region within MRI data, eliminating the need for any manual intervention. Experiments conducted on our endometriosis dataset show that our method significantly improves POD obliteration classification accuracy using MRI from AUC=0.4755 (single-modal training and testing, without automatically focusing on the uterus region) to 0.8023 (unpaired multi-modal training and single modality MRI testing, with automatic uterus region detection), while maintaining the accuracy using TVUS with AUC=0.8921 (single modality TVUS testing using either an unpaired multi-modal training or a single-modality training).

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Condition tags

endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis

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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Cited by (1)

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