Overfitting is a significant challenge in the application of Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis. Visualizing attention heatmaps reveals that current MIL methods focus on a subset of discriminative instances, hindering effective model generalization. To tackle this, we propose Attention-Challenging MIL (ACMIL), aimed at forcing the attention mechanism to focus on more challenging instances. ACMIL incorporates two techniques, Multiple Branch Attention (MBA) to capture more discriminative instances and Stochastic Top-K Instance Masking (STKIM) to suppress top-k salient instances. Evaluation on three WSI datasets with two pre-trained backbones outperforms state-of-the-art methods. Additionally, through heatmap visualization and UMAP visualization, this paper comprehensively illustrates ACMIL's effectiveness in overcoming the overfitting challenge. The source code is available at \url{https://github.com/dazhangyu123/ACMIL}.

翻译：过拟合是多实例学习（MIL）方法在全切片图像（WSI）分析中面临的一个重要挑战。可视化注意力热图显示，现有的MIL方法仅关注一部分具有判别性的实例，这阻碍了模型的有效泛化。为解决此问题，我们提出了面向注意力挑战的MIL（ACMIL），旨在强制注意力机制关注更具挑战性的实例。ACMIL集成了两种技术：多分支注意力（MBA）以捕获更多判别性实例，以及随机Top-K实例掩码（STKIM）以抑制前k个显著实例。在三个WSI数据集上使用两种预训练主干网络的评估结果表明，ACMIL性能优于现有最先进方法。此外，通过热图可视化和UMAP可视化，本文全面展示了ACMIL在克服过拟合挑战方面的有效性。源代码可从 \url{https://github.com/dazhangyu123/ACMIL} 获取。