Multi-head Attention-based Deep Multiple Instance Learning

2024-04-08 · Hassan Keshvarikhojasteh, Josien Pluim, Mitko Veta

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.

📄 PDF Abstract BibTeX arXiv:2404.05362

Code (1)

tueimage/mad-mil 공식 구현 pytorch

Tasks

DiversityMultiple Instance Learningwhole slide images

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