| Autor: |
Pan Y; The Pennsylvania State University, University Park, Pennsylvania, USA., Cai T; The Pennsylvania State University, University Park, Pennsylvania, USA., Mehta M; The Pennsylvania State University, University Park, Pennsylvania, USA., Gernand AD; The Pennsylvania State University, University Park, Pennsylvania, USA., Goldstein JA; Northwestern University, Chicago, Illinois, USA., Mithal L; Lurie Children's Hospital, Chicago, Illinois, USA., Mwinyelle D; The University of Chicago, Chicago, Illinois, USA., Gallagher K; The Pennsylvania State University, University Park, Pennsylvania, USA., Wang JZ; The Pennsylvania State University, University Park, Pennsylvania, USA. |
| Abstrakt: |
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. |
