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Multi-head Attention-based Deep Multiple Instance Learning
April 9, 2024, 4:47 a.m. | Hassan Keshvarikhojasteh, Josien Pluim, Mitko Veta
cs.CV updates on arXiv.org arxiv.org
Abstract: 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 …
arxiv attention cs.cv head instance multi-head multi-head attention multiple type
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