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层面存在标签（如患者预后或组织学分级）的任务，通常采用图块级数据的弱监督学习。在此背景下，需要提升此类模型预测结果的空间可解释性。我们提出了一种新颖的模型解释方法——全切片图像区域选择方法（Wsi rEgion sElection aPproach, WEEP）。该方法提供了一种原则性且直观的方式，用于确定为分配特定预测标签所需的WSI空间区域。我们在乳腺癌计算病理学领域的二分类任务中验证了WEEP的有效性。该方法易于实现，直接关联于基于模型的决策过程，并为研究和诊断应用提供了相关信息。