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

Yimu Pan, Tongan Cai, Manas Mehta, Alison D Gernand, Jeffery A Goldstein, Leena Mithal, Delia Mwinyelle, Kelly Gallagher, James Z Wang
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Abstract

The placenta is a valuable organ that can aid in understanding adverse events during pregnancy and predicting issues post-birth. Manual pathological examination and report generation, however, are laborious and resource-intensive. Limitations in diagnostic accuracy 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 employ efficient neural networks as image encoders to achieve model compression and inference acceleration. Experiments validate that the proposed approach outperforms prior 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.

通过视觉语言对比学习中的分布特征重组增强胎盘自动分析能力
胎盘是一个宝贵的器官,有助于了解孕期不良事件和预测产后问题。然而,人工病理检查和报告生成既费力又耗费资源。诊断准确性和模型效率方面的局限性阻碍了之前对胎盘进行自动化分析的尝试。本研究提出了一种新颖的胎盘图像自动分析框架,旨在提高准确性和效率。在之前的视觉语言对比学习(VLC)方法的基础上,我们提出了两种增强方法,即病理报告特征重组和分布特征重组,这两种方法都能提高表示的鲁棒性并减轻特征抑制。此外,我们还采用了高效的神经网络作为图像编码器,以实现模型压缩和推理加速。实验验证了所提出的方法在性能和效率上都大大优于之前的研究成果。我们方法的优势,包括更高的功效和可部署性,可能会对生殖保健产生重大影响,尤其是在农村地区或中低收入国家。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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