用专家知识引导病理图像分析，提升癌症分型准确率。
Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysis
- 结合医学文献生成疾病特异性概念，与可学习概念互补
- 在肺癌分型等任务中显著超越现有最优方法
- 适合需要可解释性病理诊断的研究者和临床医生
由于全切片图像（WSIs）尺寸大且缺乏细粒度标注，其分析通常被当作多实例学习（MIL）问题处理。然而，以往研究仅依赖训练数据学习，与人类病理学家相互教学和推理的方式相去甚远。本文提出一种基于知识概念的新型MIL框架ConcepPath，利用GPT-4从医学文献中提取可靠、特定疾病的专家级概念，并与一组纯可学习概念结合，从训练数据中提取互补知识。ConcepPath通过病理视觉-语言模型将WSIs与这些语义知识概念对齐。在肺癌亚型分类、乳腺癌HER2评分及胃癌免疫治疗敏感性亚型分类任务中，ConcepPath显著优于缺乏人类专家知识指导的现有SOTA方法。
原文摘要 · Abstract (English)
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.
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