  This paper introduces MAD-MIL, a Multi-head Attention-based Deep Multiple
Instance Learning model, designed for weakly supervised Whole Slide Images
(WSIs) classification in digital pathology. Inspired by the multi-head
attention mechanism of the Transformer, MAD-MIL simplifies model complexity
while achieving competitive results against advanced models like CLAM and
DS-MIL. Evaluated on the MNIST-BAGS and public datasets, including TUPAC16,
TCGA BRCA, TCGA LUNG, and TCGA KIDNEY, MAD-MIL consistently outperforms ABMIL.
This demonstrates enhanced information diversity, interpretability, and
efficiency in slide representation. The model's effectiveness, coupled with
fewer trainable parameters and lower computational complexity makes it a
promising solution for automated pathology workflows. Our code is available at
https://github.com/tueimage/MAD-MIL.
