Multi-head Attention-based Deep Multiple Instance Learning

• URL: http://arxiv.org/abs/2404.05362v1
• Date: Mon, 8 Apr 2024 09:54:28 GMT
• Title: Multi-head Attention-based Deep Multiple Instance Learning
• Authors: Hassan Keshvarikhojasteh, Josien Pluim, Mitko Veta,
• Abstract summary: MAD-MIL is a Multi-head Attention-based Deep Multiple Instance Learning model. It is designed for weakly supervised Whole Slide Images (WSIs) classification in digital pathology. evaluated on the MNIST-BAGS and public datasets, including TUPAC16, TCGA BRCA, TCGA LUNG, and TCGA KIDNEY.
• Score: 1.0389304366020162
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 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.

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