Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology

2025-05-28

AI Summary

Gastrointestinal (GI) pathology diagnosis suffers from high subjectivity, low reproducibility, and a lack of specialized AI models. To address these challenges, we propose Digepath—the first subspecialty foundation model for GI pathology—introduced with a novel two-stage iterative optimization framework: self-supervised pretraining followed by curated fine-tuning, specifically designed for sparse lesion detection. Pretrained on 353 million H&E image patches, Digepath integrates multi-task fine-tuning, resolution-agnostic tissue classification, and an intelligent early-cancer screening module. It is the first model to unify diagnosis, molecular phenotyping, somatic mutation inference, and prognostic assessment within a single architecture. Evaluated across 34 GI pathology tasks, Digepath achieves state-of-the-art performance on 33. In multicenter validation across nine hospitals, it attains 99.6% sensitivity for early gastric cancer detection and significantly improves diagnostic consistency and accuracy for challenging cases.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Large Vision Models
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Gastrointestinal (GI) diseases represent a clinically significant burden, necessitating precise diagnostic approaches to optimize patient outcomes. Conventional histopathological diagnosis, heavily reliant on the subjective interpretation of pathologists, suffers from limited reproducibility and diagnostic variability. To overcome these limitations and address the lack of pathology-specific foundation models for GI diseases, 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 more than 353 million image patches from over 200,000 hematoxylin and eosin-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, molecular prediction, gene mutation prediction, and prognosis evaluation, particularly in diagnostically ambiguous cases and resolution-agnostic tissue classification.We further translate the intelligent screening module for early GI cancer and achieve near-perfect 99.6% sensitivity across 9 independent medical institutions nationwide. The outstanding performance of Digepath highlights its potential to bridge critical gaps in histopathological practice. This work not only advances AI-driven precision pathology for GI diseases but also establishes a transferable paradigm for other pathology subspecialties.

Problem

• Develops a specialized AI model for gastrointestinal pathology diagnosis
• Addresses variability in histopathological diagnosis with deep learning
• Enhances early cancer detection accuracy across multiple institutions

Innovation

• Dual-phase iterative optimization strategy
• Pretrained on 353 million GI image patches
• 99.6% sensitivity in early cancer screening