Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning
- URL: http://arxiv.org/abs/2407.14904v1
- Date: Sat, 20 Jul 2024 15:34:52 GMT
- Title: Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning
- Abstract summary: SongCi is an innovative visual-language model (VLM) designed specifically for forensic pathology.
SongCi utilizes advanced cross-modal self-supervised contrastive learning to enhance the accuracy, efficiency, and generalizability of forensic analyses.
- Score: 20.3729821685966
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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