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<p>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,&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月23日

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  • 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

摘要

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.