Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology
Journal:
arXiv
Published Date:
May 28, 2025
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.
