用于可解释且具判别性癌症生存预测的多模态特征原型学习
Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction
AI总结：
本研究提出多模态框架FeatProto，通过三项创新改进原型学习，在四个公开癌症数据集上，实现了更准确可解释的癌症生存预测，为医疗原型学习提供新视角。
摘要：
生存分析在临床决策中至关重要，但当前使用的模型往往难以解释，降低了其在临床场景中的实用性。原型学习是潜在的解决方案，然而传统方法聚焦于局部相似性与静态匹配，忽略了更广泛的肿瘤背景，且缺乏与基因组数据的强语义对齐。为克服这些问题，我们提出一种创新的基于原型的多模态框架FeatProto，旨在通过解决病理学领域当前原型学习方法的重大局限，提升癌症生存预测性能。该框架建立了统一的特征原型空间，整合了全切片图像（WSI）的全局与局部特征以及基因组特征，此整合过程支持可追溯、可解释的决策过程。我们的方法包含三项核心创新：（1）稳健的表型表示，将关键图像块与全局上下文融合，并与基因组数据协调以最小化局部偏差；（2）指数型原型更新策略（EMA ProtoUp），其维持稳定的跨模态关联，并采用游走机制使原型灵活适应肿瘤异质性；（3）分层原型匹配方案，旨在捕捉全局中心性、局部典型性和队列级趋势，从而优化原型推理。对四个公开癌症数据集的综合评估表明，我们的方法在准确性和可解释性两方面均超越了当前领先的单模态及多模态生存预测技术，为关键医疗应用中的原型学习提供了新视角。我们的源代码可在该https链接获取。
英文摘要：
Survival analysis plays a vital role in making clinical decisions. However, the models currently in use are often difficult to interpret, which reduces their usefulness in clinical settings. Prototype learning presents a potential solution, yet traditional methods focus on local similarities and static matching, neglecting the broader tumor context and lacking strong semantic alignment with genomic data. To overcome these issues, we introduce an innovative prototype-based multimodal framework, FeatProto, aimed at enhancing cancer survival prediction by addressing significant limitations in current prototype learning methodologies within pathology. Our framework establishes a unified feature prototype space that integrates both global and local features of whole slide images (WSI) with genomic profiles. This integration facilitates traceable and interpretable decision-making processes. Our approach includes three main innovations: (1) A robust phenotype representation that merges critical patches with global context, harmonized with genomic data to minimize local bias. (2) An Exponential Prototype Update Strategy (EMA ProtoUp) that sustains stable cross-modal associations and employs a wandering mechanism to adapt prototypes flexibly to tumor heterogeneity. (3) A hierarchical prototype matching scheme designed to capture global centrality, local typicality, and cohort-level trends, thereby refining prototype inference. Comprehensive evaluations on four publicly available cancer datasets indicate that our method surpasses current leading unimodal and multimodal survival prediction techniques in both accuracy and interpretability, providing a new perspective on prototype learning for critical medical applications. Our source code is available at https://github.com/JSLiam94/FeatProto.
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。
