Enhancing Automatic Placenta Analysis through Distributional Feature Recomposition
in Vision-Language Contrastive Learning

Yimu Pan (1), Tongan Cai (1), Manas Mehta (1), Alison D. Gernand (1), Jeffery A. Goldstein (2), Leena Mithal (3), Delia Mwinyelle (4), Kelly Gallagher (1), and James Z. Wang (1)

(1) The Pennsylvania State University, University Park, Pennsylvania, USA
(2) Northwestern University, Chicago, Illinois, USA
(3) Lurie Children’s Hospital, Illinois, USA
(4) The University of Chicago, Illinois, USA

The placenta is a valuable organ that can aid in understanding adverse events during a pregnancy and predicting adverse events after birth. However, manual pathological examination and report generation is laborious and resource-intensive. Limitations in diagnostic performance and model efficiency have impeded previous attempts to automate placenta analysis. This study presents a novel framework for the automatic analysis of placenta images that aims to improve accuracy and efficiency. Building on previous vision-language contrastive learning (VLC) methods, we propose two enhancements, namely Pathology Report Feature Recomposition and Distributional Feature Recomposition, which increase representation robustness and mitigate feature suppression. In addition, we deploy efficient neural networks as image encoders to achieve model compression and inference acceleration. Experiments demonstrate that the proposed approach outperforms previous work in both performance and efficiency by significant margins. The benefits of our method, including enhanced efficacy and deployability, may have significant implications for reproductive healthcare, particularly in rural areas or low- and middle-income countries.

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Citation: Yimu Pan, Tongan Cai, Manas Mehta, Alison D. Gernand, Jeffery A. Goldstein, Leena Mithal, Delia Mwinyelle, Kelly Gallagher and James Z. Wang, ``Enhancing Automatic Placenta Analysis through Distributional Feature Recomposition in Vision-Language Contrastive Learning,'' Proceedings of the International Conference on Medical Image Computing and Computer Assisted Interventions, Lecture Notes in Computer Science, Lecture Notes in Computer Science, vol. 14225, Hayit Greenspan et al. (eds.), pp. 116-126, Vancouver, Canada, October 2023.

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Last Modified: October 18, 2023
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