Abstract
Due to the large size and lack of fine-grained annotation, Whole Slide Images (WSIs) analysis is commonly approached as a Multiple Instance Learning (MIL) problem. However, previous studies only learn from training data, posing a stark contrast to how human clinicians teach each other and reason about histopathologic entities and factors. Here, we present a novel knowledge concept-based MIL framework, named ConcepPath, to fill this gap. Specifically, ConcepPath utilizes GPT-4 to induce reliable disease-specific human expert concepts from medical literature and incorporate them with a group of purely learnable concepts to extract complementary knowledge from training data. In ConcepPath, WSIs are aligned to these linguistic knowledge concepts by utilizing the pathology vision-language model as the basic building component. In the application of lung cancer subtyping, breast cancer HER2 scoring, and gastric cancer immunotherapy-sensitive subtyping tasks, ConcepPath significantly outperformed previous SOTA methods, which lacked the guidance of human expert knowledge.