  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.
