超越注意力热图：如何为多实例学习模型在病理学中的更好解释
Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology
AI总结：
本研究提出了一种评估MIL热图质量的通用框架，发现扰动、LRP和IG在病理学任务中表现更优，展示了其在生物验证和发现不同模型策略中的应用。
摘要：
多实例学习（MIL）已在计算病理学中实现了显著进展，其中大量来自吉pix全片图像的切片被聚合为滑片级预测。热图被广泛用于验证MIL模型并发现组织生物标记物。然而，这些热图的有效性却几乎未被调查。在本工作中，我们介绍了一个通用框架，用于评估MIL热图的质量，而无需额外标签。我们进行了一项大规模基准测试实验，评估了六种解释方法在病理学任务类型（分类、回归、生存）、MIL模型架构（注意力-、Transformer-、Mamba-based）和切片编码器骨干（UNI2、Virchow2）上的表现。我们的结果表明，解释质量主要取决于MIL模型架构和任务类型，其中扰动（
英文摘要：
Multiple instance learning (MIL) has enabled substantial progress in computational histopathology, where a large amount of patches from gigapixel whole slide images are aggregated into slide-level predictions. Heatmaps are widely used to validate MIL models and to discover tissue biomarkers. Yet, the validity of these heatmaps has barely been investigated. In this work, we introduce a general framework for evaluating the quality of MIL heatmaps without requiring additional labels. We conduct a large-scale benchmark experiment to assess six explanation methods across histopathology task types (classification, regression, survival), MIL model architectures (Attention-, Transformer-, Mamba-based), and patch encoder backbones (UNI2, Virchow2). Our results show that explanation quality mostly depends on MIL model architecture and task type, with perturbation ("Single"), layer-wise relevance propagation (LRP), and integrated gradients (IG) consistently outperforming attention-based and gradient-based saliency heatmaps, which often fail to reflect model decision mechanisms. We further demonstrate the advanced capabilities of the best-performing explanation methods: (i) We provide a proof-of-concept that MIL heatmaps of a bulk gene expression prediction model can be correlated with spatial transcriptomics for biological validation, and (ii) showcase the discovery of distinct model strategies for predicting human papillomavirus (HPV) infection from head and neck cancer slides. Our work highlights the importance of validating MIL heatmaps and establishes that improved explainability can enable more reliable model validation and yield biological insights, making a case for a broader adoption of explainable AI in digital pathology. Our code is provided in a public GitHub repository: https://github.com/bifold-pathomics/xMIL/tree/xmil-journal
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。
