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 diseasespecific 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 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 task, ConcepPath significantly outperformed previous SOTA methods which lack the guidance of human expert knowledge.

本研究解决了全切片图像分析中由于缺乏细粒度注释而导致的多实例学习问题。提出的ConcepPath框架通过整合医学生文献中提取的专家知识概念与可学习概念，来增强模型的知识提取能力。研究结果表明，ConcepPath在肺癌亚型、乳腺癌HER2评分和胃癌免疫治疗敏感性亚型任务中显著超越了缺乏专家知识指导的现有最先进方法。

将知识概念与全切片图像对齐以实现精确的组织病理图像分析