Multi-scale Attention-Based Multiple Instance Learning for Breast Cancer Diagnosis

Mariana Mourão, Jacinto C. Nascimento, Carlos Santiago, Margarida Silveira

Abstract: Multiple Instance Learning (MIL) is a powerful weakly supervised learning framework for high-resolution medical images, but its application in mammographic breast cancer (BC) diagnosis overlooks instance interactions and the multi-scale nature of BC lesions. In this work, we propose a novel Feature Pyramid Network (FPN)-MIL model for BC classification and detection in high-resolution mammograms, integrating (1) a FPN-based instance encoder that enables a multi-scale analysis across different receptive-field granularities while operating on single-scale input patches; (2) deep-supervised scale-specific instance aggregators that support conventional attention (AbMIL) or transformer-based (SetTrans) mechanisms; (3) an attention-based multi-scale aggregator that dynamically combines scale-specific features, improving robustness to lesion scale variability. Our experiments show that FPN-MIL is superior to conventional single- and multi-scale patch-based MIL models, with FPN-SetTrans outperforming baselines in calcification classification and detection while FPN-AbMIL performs best for mass classification. Code is available publicly at: https://github.com/marianamourao-37/Multi-scale-Attention-based-MIL.

External IDs:dblp:conf/miccai/MouraoNSS25