Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning
Abstract 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 pape...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62060-x |
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| Summary: | Abstract 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. |
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| ISSN: | 2041-1723 |