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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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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| author | 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 |
| author_facet | 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 |
| author_sort | Chen Shen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fea06ba2e7a646ea90e22c8eb883ca59 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-fea06ba2e7a646ea90e22c8eb883ca592025-08-20T03:05:10ZengNature PortfolioNature Communications2041-17232025-07-0116112010.1038/s41467-025-62060-xLarge-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learningChen Shen0Chunfeng Lian1Wanqing Zhang2Fan Wang3Jianhua Zhang4Shuanliang Fan5Xin Wei6Gongji Wang7Kehan Li8Hongshu Mu9Hao Wu10Xinggong Liang11Jianhua Ma12Zhenyuan Wang13Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi’an Jiaotong UniversitySchool of Mathematics and Statistics, Xi’an Jiaotong UniversityKey Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi’an Jiaotong UniversityKey Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong UniversityShanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic ScienceKey Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi’an Jiaotong UniversityKey Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi’an Jiaotong UniversityKey Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi’an Jiaotong UniversityKey Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong UniversityWeicheng Branch, Xian’yang Public Security BureauKey Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi’an Jiaotong UniversityKey Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi’an Jiaotong UniversityPazhou Lab (Huangpu)Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi’an Jiaotong UniversityAbstract 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.https://doi.org/10.1038/s41467-025-62060-x |
| spellingShingle | 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 Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning Nature Communications |
| title | Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning |
| title_full | Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning |
| title_fullStr | Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning |
| title_full_unstemmed | Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning |
| title_short | Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning |
| title_sort | large vocabulary forensic pathological analyses via prototypical cross modal contrastive learning |
| url | https://doi.org/10.1038/s41467-025-62060-x |
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