鉴于数字化全扫描切片图像（WSIs）的大小，病理学家在其中彻底勾画物体时通常需要耗费大量时间和精力，尤其是在包含数百个幻灯片的数据集中进行注释。在大多数情况下，只有幻灯片级别的标签可用，针对此种情况开发了弱监督模型。然而，从这样的模型中获得准确的物体定位通常很困难，例如在肿瘤检测任务中带有肿瘤细胞的补丁，因为它们主要设计用于幻灯片级别的分类。本文以基于注意力的深度多实例学习（MIL）模型作为弱监督模型，提出使用混合督导策略-即同时使用幻灯片级别和补丁级别的标签-在仅使用有限量的补丁级别标注的幻灯片的情况下，提高原始模型的分类和定位性能。此外，我们提出一种注意力损失项，以规范关键实例之间的注意力，并提出一种配对批处理方法，用于为模型创建平衡批次。首先，我们展示了对模型所作的更改已经改进了其在弱监督环境中的性能和可解释性。此外，在使用总可用补丁级别注释量的12％至62％之间时，我们可以在消化道通道2019和Camelyon16肿瘤分类数据集上达到接近完全监督模型的性能。版权所有©2023 Elsevier B.V.。

Given the size of digitized Whole Slide Images (WSIs), it is generally laborious and time-consuming for pathologists to exhaustively delineate objects within them, especially with datasets containing hundreds of slides to annotate. Most of the time, only slide-level labels are available, giving rise to the development of weakly-supervised models. However, it is often difficult to obtain from such models accurate object localization, e.g., patches with tumor cells in a tumor detection task, as they are mainly designed for slide-level classification. Using the attention-based deep Multiple Instance Learning (MIL) model as our base weakly-supervised model, we propose to use mixed supervision - i.e., the use of both slide-level and patch-level labels - to improve both the classification and the localization performances of the original model, using only a limited amount of patch-level labeled slides. In addition, we propose an attention loss term to regularize the attention between key instances, and a paired batch method to create balanced batches for the model. First, we show that the changes made to the model already improve its performance and interpretability in the weakly-supervised setting. Furthermore, when using only between 12 and 62% of the total available patch-level annotations, we can reach performance close to fully-supervised models on the tumor classification datasets DigestPath2019 and Camelyon16.Copyright © 2023 Elsevier B.V. All rights reserved.