Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology

• URL: http://arxiv.org/abs/2505.21928v2
• Date: Fri, 06 Jun 2025 10:39:32 GMT
• Title: Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology
• Authors: Lianghui Zhu, Xitong Ling, Minxi Ouyang, Xiaoping Liu, Tian Guan, Mingxi Fu, Zhiqiang Cheng, Fanglei Fu, Maomao Zeng, Liming Liu, Song Duan, Qiang Huang, Ying Xiao, Jianming Li, Shanming Lu, Zhenghua Piao, Mingxi Zhu, Yibo Jin, Shan Xu, Qiming He, Yizhi Wang, Junru Cheng, Xuanyu Wang, Luxi Xie, Houqiang Li, Sufang Tian, Yonghong He,
• Abstract summary: Digepath is a specialized foundation model for GI pathology.<n>It is pretrained on over 353 million multi-scale images from 210,043 H&E-stained slides of GI diseases.<n>It attains state-of-the-art performance on 33 out of 34 tasks related to GI pathology.
• Score: 38.30990764764014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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