  Multiple Instance Learning (MIL) has demonstrated effectiveness in analyzing
whole slide images (WSIs), yet it often encounters overfitting challenges in
real-world applications. This paper reveals the correlation between MIL's
performance and the entropy of attention values. Based on this observation, we
propose Attention Diversity Regularization (ADR), a simple but effective
technique aimed at promoting high entropy in attention values. Specifically,
ADR introduces a negative Shannon entropy loss for attention values into the
regular MIL framework. Compared to existing methods aimed at alleviating
overfitting, which often necessitate additional modules or processing steps,
our ADR approach requires no such extras, demonstrating simplicity and
efficiency. We evaluate our ADR on three WSI classification tasks. ADR achieves
superior performance over the state-of-the-art on most of them. We also show
that ADR can enhance heatmaps, aligning them better with pathologists'
diagnostic criteria. The source code is available at
\url{https://github.com/dazhangyu123/ADR}.
