使用自动编码器和可解释人工智能的视觉Transformer在全切片成像中进行癌症患者风险分层
Vision Transformers with Autoencoders and Explainable AI for Cancer Patient Risk Stratification Using Whole Slide Imaging
AI总结：
本文提出PATH-X框架，结合视觉Transformer、自动编码器和SHAP，利用TCGA全切片成像进行癌症患者风险分层与预测，在乳腺癌和胶质瘤中表现良好，并提升了模型可解释性。
摘要：
癌症仍然是全球范围内导致死亡的主要原因之一，因此需要准确的诊断和预后。随着数字病理学的发展，全切片成像（WSI）已成为临床工作流程中不可或缺的一部分。尽管已有多种研究利用了WSI，但其所提取的特征可能无法充分捕捉最相关的病理信息，且缺乏可解释性限制了其在临床中的应用。在本文中，我们提出了PATH-X框架，该框架将视觉Transformer（ViT）和自动编码器与SHAP（Shapley Additive Explanations）相结合，以增强模型的可解释性，用于利用来自癌症基因组图谱（TCGA）的WSI进行患者分层和风险预测。从每个WSI中选取一张代表性图像切片，并使用Google预训练的ViT提取数值特征嵌入。随后，这些特征通过自动编码器进行压缩，并用于无监督聚类和分类任务。采用Kaplan-Meier生存分析评估分为两个和三个风险组的分层效果。使用SHAP识别关键贡献特征，并将其映射到组织病理学切片上以提供空间背景信息。PATH-X在乳腺癌和胶质瘤中表现出较强的性能，因为这两种癌症有足够数量的WSI支持稳健的分层。然而，由于数据可用性的限制，肺癌中的性能有限，这强调了需要更大规模的数据集来提高模型的可靠性和临床适用性。
英文摘要：
Cancer remains one of the leading causes of mortality worldwide, necessitating accurate diagnosis and prognosis. Whole Slide Imaging (WSI) has become an integral part of clinical workflows with advancements in digital pathology. While various studies have utilized WSIs, their extracted features may not fully capture the most relevant pathological information, and their lack of interpretability limits clinical adoption. In this paper, we propose PATH-X, a framework that integrates Vision Transformers (ViT) and Autoencoders with SHAP (Shapley Additive Explanations) to enhance model explainability for patient stratification and risk prediction using WSIs from The Cancer Genome Atlas (TCGA). A representative image slice is selected from each WSI, and numerical feature embeddings are extracted using Google's pre-trained ViT. These features are then compressed via an autoencoder and used for unsupervised clustering and classification tasks. Kaplan-Meier survival analysis is applied to evaluate stratification into two and three risk groups. SHAP is used to identify key contributing features, which are mapped onto histopathological slices to provide spatial context. PATH-X demonstrates strong performance in breast and glioma cancers, where a sufficient number of WSIs enabled robust stratification. However, performance in lung cancer was limited due to data availability, emphasizing the need for larger datasets to enhance model reliability and clinical applicability.
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