Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI). Weakly supervised learning of tile-level data is typically applied for tasks where labels only exist on the patient or WSI level (e.g. patient outcomes or histological grading). In this context, there is a need for improved spatial interpretability of predictions from such models. We propose a novel method, Wsi rEgion sElection aPproach (WEEP), for model interpretation. It provides a principled yet straightforward way to establish the spatial area of WSI required for assigning a particular prediction label. We demonstrate WEEP on a binary classification task in the area of breast cancer computational pathology. WEEP is easy to implement, is directly connected to the model-based decision process, and offers information relevant to both research and diagnostic applications.

翻译：深度学习能够对高分辨率组织病理学全切片图像（WSI）进行建模。对于仅在患者或WSI级别存在标签（例如患者预后或组织学分级）的任务，通常采用平铺级数据的弱监督学习方法。在此背景下，需要提升此类模型预测结果的空间可解释性。我们提出了一种新的模型解释方法——WSI区域选择方法（WEEP）。该方法提供了一种原则性且直接的方式来确立分配特定预测标签所需的WSI空间区域。我们通过乳腺癌计算病理学中的二分类任务展示了WEEP的应用。WEEP易于实现，直接关联基于模型的决策过程，并能提供对研究和诊断应用均有价值的信息。