Digepath模型提升胃肠病理诊断精准度，助力早期癌症筛查
Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology
- 基于双阶段优化策略，专攻全片图像中稀疏病变区域检测
- 在34项胃肠病理任务中表现领先，9家医院测试达99.7%灵敏度
- 适合临床病理科与医学AI研究者参考，推动智能病理落地
胃肠道（GI）疾病带来重大临床负担，需精确诊断以改善患者预后。传统组织病理学诊断存在可重复性差和诊断差异大等问题。为此，我们开发了针对胃肠病理的专用基础模型Digepath。该框架采用双阶段迭代优化策略，结合预训练与细筛机制，专门应对全切片图像中稀疏分布病变区域的检测难题。Digepath在超过35300万张来自210,043例H&E染色切片的多尺度图像上进行预训练。在34项胃肠病理相关任务中取得33项最优表现，涵盖病理诊断、蛋白表达状态预测、基因突变预测及预后评估。进一步将智能筛查模块应用于早期胃癌检测，在九家独立医疗机构实现近乎完美的99.70%敏感度。本研究不仅推进了胃肠疾病人工智能驱动的精准病理学发展，也弥合了组织病理实践中的关键空白。
原文摘要 · Abstract (English)
Gastrointestinal (GI) diseases represent a clinically significant burden, necessitating precise diagnostic approaches to optimize patient outcomes. Conventional histopathological diagnosis suffers from limited reproducibility and diagnostic variability. To overcome these limitations, we develop Digepath, a specialized foundation model for GI pathology. Our framework introduces a dual-phase iterative optimization strategy combining pretraining with fine-screening, specifically designed to address the detection of sparsely distributed lesion areas in whole-slide images. Digepath is pretrained on over 353 million multi-scale images from 210,043 H&E-stained slides of GI diseases. It attains state-of-the-art performance on 33 out of 34 tasks related to GI pathology, including pathological diagnosis, protein expression status prediction, gene mutation prediction, and prognosis evaluation. We further translate the intelligent screening module for early GI cancer and achieve near-perfect 99.70% sensitivity across nine independent medical institutions. This work not only advances AI-driven precision pathology for GI diseases but also bridge critical gaps in histopathological practice.
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