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.

利用深度学习对高分辨率组织病理学全幻灯片图像进行建模，提出一种适用于模型解读的方法，Wsi rEgion sElection aPproach (WEEP)，该方法能够准确确定用于预测特定标签所需的 WSI 空间范围，并且在乳腺癌计算病理学领域的二分类任务中进行了演示。