Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning

TL;DR

This paper introduces SongCi, a novel visual-language model tailored for forensic pathology, leveraging cross-modal contrastive learning to improve analysis accuracy, efficiency, and explainability on large-scale forensic datasets.

Contribution

SongCi is the first large-vocabulary VLM specifically designed for forensic pathology, capable of processing gigapixel WSIs and outperforming existing models and less experienced pathologists.

Findings

SongCi surpasses existing multi-modal AI models in forensic tasks.

Performs comparably to experienced forensic pathologists.

Provides detailed multi-modal explainability.

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.