Multi-head Attention-based Deep Multiple Instance Learning
<|reference_start|>Multi-head Attention-based Deep Multiple Instance Learning: 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.<|reference_end|>
@article{keshvarikhojasteh2024multi-head,
    title={Multi-head Attention-based Deep Multiple Instance Learning},
    author={Hassan Keshvarikhojasteh, Josien Pluim, Mitko Veta},
    journal={arXiv preprint arXiv:2404.05362},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05362},
    primaryClass={cs.CV}
}