In computational pathology, whole slide image (WSI) classification presents a formidable challenge due to its gigapixel resolution and limited fine-grained annotations. Multiple instance learning (MIL) offers a weakly supervised solution, yet refining instance-level information from bag-level labels remains complex. While most of the conventional MIL methods use attention scores to estimate instance importance scores (IIS) which contribute to the prediction of the slide labels, these often lead to skewed attention distributions and inaccuracies in identifying crucial instances. To address these issues, we propose a new approach inspired by cooperative game theory: employing Shapley values to assess each instance's contribution, thereby improving IIS estimation. The computation of the Shapley value is then accelerated using attention, meanwhile retaining the enhanced instance identification and prioritization. We further introduce a framework for the progressive assignment of pseudo bags based on estimated IIS, encouraging more balanced attention distributions in MIL models. Our extensive experiments on CAMELYON-16, BRACS, and TCGA-LUNG datasets show our method's superiority over existing state-of-the-art approaches, offering enhanced interpretability and class-wise insights. We will release the code upon acceptance.

翻译：在计算病理学中，全切片图像分类因其十亿像素级分辨率及有限的细粒度标注而面临严峻挑战。多实例学习提供了一种弱监督解决方案，但从袋级标签中细化实例级信息仍存在复杂性。传统多实例学习方法多采用注意力得分估计实例重要性得分以辅助切片标签预测，但这常导致注意力分布偏斜及关键实例识别偏差。针对这些问题，我们提出一种受合作博弈理论启发的新方法：通过Shapley值评估各实例贡献度，从而改进实例重要性得分估计。进而利用注意力机制加速Shapley值计算，同时保留增强的实例识别与优先级排序能力。我们进一步提出基于估计实例重要性得分的渐进式伪袋分配框架，促使多实例学习模型获得更均衡的注意力分布。在CAMELYON-16、BRACS及TCGA-LUNG数据集上的广泛实验表明，该方法优于现有最先进技术，具备更强的可解释性与类别级洞察力。论文被接收后将公开代码。