  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.
