Multi-head Attention-based Deep Multiple Instance Learning
This addresses automated pathology workflows for medical professionals, but it is incremental as it builds on existing attention-based methods.
The paper tackles weakly supervised classification of Whole Slide Images in digital pathology by introducing MAD-MIL, a model that simplifies complexity and achieves competitive results, consistently outperforming ABMIL on datasets like TUPAC16 and TCGA.
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.