In many histopathology tasks, sample classification depends on morphological details in tissue or single cells that are only visible at the highest magnification. For a pathologist, this implies tedious zooming in and out, while for a computational decision support algorithm, it leads to the analysis of a huge number of small image patches per whole slide image (WSI). Attention-based multiple instance learning (MIL), where attention estimation is learned in a weakly supervised manner, has been successfully applied in computational histopathology, but it is challenged by large numbers of irrelevant patches, reducing its accuracy. Here, we present an active learning approach to the problem. Querying the expert to annotate regions of interest in a WSI guides the formation of high-attention regions for MIL. We train an attention-based MIL and calculate a confidence metric for every image in the dataset to select the most uncertain WSIs for expert annotation. We test our approach on the CAMELYON17 dataset classifying metastatic lymph node sections in breast cancer. With a novel attention guiding loss, this leads to an accuracy boost of the trained models with few regions annotated for each class. Active learning thus improves WSIs classification accuracy, leads to faster and more robust convergence, and speeds up the annotation process. It may in the future serve as an important contribution to train MIL models in the clinically relevant context of cancer classification in histopathology.

翻译：在众多组织病理学任务中，样本分类依赖于仅在高倍镜下可见的组织或单细胞形态细节。对病理学家而言，这意味着繁琐的缩放操作；而对计算决策支持算法来说，则需对每张全切片图像（WSI）分析海量的小图像块。基于注意力机制的多示例学习（MIL）通过弱监督方式学习注意力估计，已成功应用于计算组织病理学，但大量无关图像块会降低其准确性。本文提出一种主动学习方法：通过专家标注WSI中感兴趣区域，引导MIL形成高注意力区域。我们训练基于注意力的MIL模型，并计算数据集中每张图像的置信度指标，以选择最不确定的WSI供专家标注。在CAMELYON17数据集上测试乳腺癌淋巴结转移灶分类任务时，结合新型注意力引导损失函数，每个类别仅需标注少量区域即可显著提升模型准确性。主动学习不仅提高了WSI分类精度，还实现了更快速、更稳定的收敛，并加速了标注流程。该方法未来可望在组织病理学癌症分类这一临床相关场景中，为训练MIL模型提供重要支撑。