基于原型跨模态对比学习的大词汇量法医病理学分析
Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning
AI总结：
本文提出专为法医病理学设计的视觉语言模型SongCi，通过原型跨模态自监督对比学习，在多中心大规模数据上预训练，在多项法医任务中超越现有AI模型，表现媲美资深病理学家，是首个直接处理千兆像素全切片图像的大词汇量计算病理学模型。
摘要：
法医病理学通过宏观和微观的尸检来确定死亡原因和方式，至关重要。然而，该领域面临结果变异性、繁琐流程和缺乏训练有素的专业人员等问题。本文提出了SongCi，一种专为法医病理学设计的创新视觉语言模型（VLM）。SongCi利用先进的原型跨模态自监督对比学习，提高法医分析的准确性、效率和泛化能力。它在一个综合的多中心数据集上进行了预训练和评估，该数据集包含超过1600万个高分辨率图像块、2228对死后全切片图像（WSIs）与相应大体关键发现的视觉语言对，以及471种不同的诊断结果。研究结果表明，SongCi在许多法医病理学任务中超越了现有的多模态AI模型，表现与经验丰富的法医病理学家相当，且显著优于经验不足者，并提供详细的多模态可解释性，为法医调查提供关键帮助。据我们所知，SongCi是首个专为法医病理分析开发的VLM，也是法医学中首个直接处理千兆像素WSIs的大词汇量计算病理学（CPath）模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
