2024-11-17-keshvarikhojasteh24a.md

title	booktitle	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Multi-head Attention-based Deep Multiple Instance Learning	Proceedings of the MICCAI Workshop on Computational Pathology	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	keshvarikhojasteh24a	0	Multi-head Attention-based Deep Multiple Instance Learning	1	12	1-12	1	false	Keshvarikhojasteh, Hassan and Pluim, Josien P. W. and Veta, Mitko	given family Hassan Keshvarikhojasteh given family Josien P. W. Pluim given family Mitko Veta	2024-11-17		Proceedings of the MICCAI Workshop on Computational Pathology	254	inproceedings	date-parts 2024 11 17	https://raw.githubusercontent.com/mlresearch/v254/main/assets/keshvarikhojasteh24a/keshvarikhojasteh24a.pdf