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
this https URL

MAD-MIL 是一个基于多头注意力机制的深度多实例学习模型，针对数字病理学中弱监督的整张切片图像分类而设计。在模型复杂度简化的同时，MAD-MIL 能够与先进模型如 CLAM 和 DS-MIL 取得竞争性结果，并在 MNIST-BAGS 和公共数据集（包括 TUPAC16、TCGA BRCA、TCGA LUNG 和 TCGA KIDNEY）上对 ABMIL 进行持续超越。该模型展示了切片表示中的信息多样性、可解释性和效率的提升。结合较少的可训练参数和较低的计算复杂度，这使得该模型成为自动化病理工作流程的有希望的解决方案。我们的代码可在此 https URL 找到。