PA-MIL：基于语言提示和基因型-表型关系的表型感知多实例学习
PA-MIL: Phenotype-Aware Multiple Instance Learning Guided by Language Prompting and Genotype-to-Phenotype Relationships
AI总结：
PA-MIL通过结合语言提示和基因型-表型关系，实现对癌症表型的前瞻性可解释性学习，提升全切片图像分析的可靠性与可问责性。
摘要：
深度学习在病理全切片图像（WSI）分析中已得到广泛研究。然而，大多数现有方法仅通过事后定位模型的显著区域来提供预测可解释性，未能提供更可靠和可问责的解释。在本工作中，我们提出了表型感知多实例学习（PA-MIL），一种新的前瞻性可解释框架，从WSI中识别与癌症相关的表型，并利用它们进行癌症亚型分类。为了促进PA-MIL学习表型感知特征，我们1）构建包含癌症相关表型及其关联基因型的表型知识库。2）利用表型的形态学描述作为语言提示来聚合与表型相关的特征。3）设计基于基因型-表型关系的基因型到表型神经网络（GP-NN），为PA-MIL提供多级指导。在多个数据集上的实验结果表明，PA-MIL在性能上与现有MIL方法竞争，同时提供改进的可解释性。PA-MIL利用表型显著性作为证据，并使用线性分类器，实现了与最先进方法相媲美的结果。此外，我们全面分析了基因型-表型关系，以及群体层面和病例层面的可解释性，证明了PA-MIL的可靠性和可问责性。
英文摘要：
Deep learning has been extensively researched in the analysis of pathology whole-slide images (WSIs). However, most existing methods are limited to providing prediction interpretability by locating the model's salient areas in a post-hoc manner, failing to offer more reliable and accountable explanations. In this work, we propose Phenotype-Aware Multiple Instance Learning (PA-MIL), a novel ante-hoc interpretable framework that identifies cancer-related phenotypes from WSIs and utilizes them for cancer subtyping. To facilitate PA-MIL in learning phenotype-aware features, we 1) construct a phenotype knowledge base containing cancer-related phenotypes and their associated genotypes. 2) utilize the morphological descriptions of phenotypes as language prompting to aggregate phenotype-related features. 3) devise the Genotype-to-Phenotype Neural Network (GP-NN) grounded in genotype-to-phenotype relationships, which provides multi-level guidance for PA-MIL. Experimental results on multiple datasets demonstrate that PA-MIL achieves competitive performance compared to existing MIL methods while offering improved interpretability. PA-MIL leverages phenotype saliency as evidence and, using a linear classifier, achieves competitive results compared to state-of-the-art methods. Additionally, we thoroughly analyze the genotype-phenotype relationships, as well as cohort-level and case-level interpretability, demonstrating the reliability and accountability of PA-MIL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
