Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning
<|reference_start|>Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning: Forensic pathology is critical in determining the cause and manner of death through post-mortem examinations, both macroscopic and microscopic. The field, however, grapples with issues such as outcome variability, laborious processes, and a scarcity of trained professionals. This paper presents SongCi, an innovative visual-language model (VLM) designed specifically for forensic pathology. SongCi utilizes advanced prototypical cross-modal self-supervised contrastive learning to enhance the accuracy, efficiency, and generalizability of forensic analyses. It was pre-trained and evaluated on a comprehensive multi-center dataset, which includes over 16 million high-resolution image patches, 2,228 vision-language pairs of post-mortem whole slide images (WSIs), and corresponding gross key findings, along with 471 distinct diagnostic outcomes. Our findings indicate that SongCi surpasses existing multi-modal AI models in many forensic pathology tasks, performs comparably to experienced forensic pathologists and significantly better than less experienced ones, and provides detailed multi-modal explainability, offering critical assistance in forensic investigations. To the best of our knowledge, SongCi is the first VLM specifically developed for forensic pathological analysis and the first large-vocabulary computational pathology (CPath) model that directly processes gigapixel WSIs in forensic science.<|reference_end|>
@article{shen2024large-vocabulary,
    title={Large-vocabulary forensic pathological analyses via prototypical
  cross-modal contrastive learning},
    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},
    journal={arXiv preprint arXiv:2407.14904},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14904},
    primaryClass={eess.IV cs.AI cs.CL cs.CV}
}