Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology
Abstrak
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.
Penulis (27)
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
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓