Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning

作者：Chen Shen, Chunfeng Lian, Wanqing Zhang, Fan Wang, Jianhua Zhang, Shuanliang Fan, Xin Wei, Gongji Wang, Kehan Li, Hongshu Mu, Hao Wu, Xinggong Liang, Jianhua Ma, Zhenyuan Wang · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-62060-x · 被引用次数：12 · 研究领域：Autopsy Techniques and Outcomes、Artificial Intelligence in Healthcare and Education、AI in cancer detection

Forensic pathology plays a vital role in determining the cause and manner of death through macroscopic and microscopic post-mortem examinations. However, the field faces challenges such as variability in outcomes, labor-intensive processes, and a shortage of skilled professionals. This paper introduces SongCi, a visual-language model tailored for forensic pathology. Leveraging advanced prototypical cross-modal self-supervised contrastive learning, SongCi improves the accuracy, efficiency, and generalizability of forensic analyses. Pre-trained and validated on a large multi-center dataset comprising over 16 million high-resolution image patches, 2, 228 vision-language pairs from post-mortem whole slide images, gross key findings, and 471 unique diagnostic outcomes, SongCi demonstrates superior performance over existing multi-modal models and computational pathology foundation models in forensic tasks. It matches experienced forensic pathologists' capabilities, significantly outperforms less experienced practitioners, and offers robust multi-modal explainability.