Overfitting remains a significant challenge in the application of Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis. Visualizing heatmaps reveals that current MIL methods focus on a subset of predictive instances, hindering effective model generalization. To tackle this, we propose Attention-Challenging MIL (ACMIL), aimed at forcing the attention mechanism to capture more challenging predictive instances. ACMIL incorporates two techniques, Multiple Branch Attention (MBA) to capture richer predictive instances and Stochastic Top-K Instance Masking (STKIM) to suppress simple predictive instances. Evaluation on three WSI datasets outperforms state-of-the-art methods. Additionally, through heatmap visualization, UMAP visualization, and attention value statistics, 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：摘要：在多实例学习（MIL）方法应用于全切片图像（WSI）分析时，过拟合仍然是一个重大挑战。热力图可视化揭示，当前的MIL方法聚焦于一部分预测性实例，从而阻碍了模型的有效泛化。为解决这一问题，我们提出注意力挑战的多实例学习（ACMIL），旨在迫使注意力机制捕获更具挑战性的预测性实例。ACMIL包含两种技术：多分支注意力（MBA）以捕获更丰富的预测性实例，以及随机Top-K实例掩码（STKIM）以抑制简单的预测性实例。在三个WSI数据集上的评估结果显示，ACMIL的性能优于现有最先进方法。此外，通过热力图可视化、UMAP可视化以及注意力值统计分析，本文全面展示了ACMIL在克服过拟合挑战方面的有效性。源代码可在 \url{https://github.com/dazhangyu123/ACMIL} 获取。