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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