Existing deep learning models in digital pathology typically require extensive labeled data and show limited generalization across organs. In contrast, large vision models exhibit effective feature extraction capabilities, enabling pathological image analysis for gastrointestinal cancer with relatively small sample sizes. In this study, we developed a screening framework leveraging a large vision model for coarse-grained classification of gastric and colorectal tissues. The model was evaluated on multicenter cohorts and under limited-data conditions. Using labeled tiles from only 76 whole-slide images, the model achieved class-averaged sensitivity and precision of 0.9816 and 0.9808 on the internal test set, and 0.9161 and 0.9179 on the external test set. When trained with only 200 tiles per class from 20 wholeslide images, the model maintained comparable performance, achieving sensitivity and precision of 0.9548 and 0.9518. These findings suggest that the model has reliable performance across multicenter cohorts and potential applicability in clinical pathology workflows.
Liu et al. (Thu,) studied this question.