Complexity-Efficient Deep Learning for Breast Cancer Detection Using BI-RADS Descriptors

Deep learning has achieved substantial progress in breast cancer detection from mammograms, with recent state-of-the-art methods leveraging multiple views, such as craniocaudal (CC) and mediolateral oblique (MLO), to improve diagnostic accuracy. Most existing approaches pursue performance gains by increasing architectural complexity and rely exclusively on image data. In contrast, clinical practice heavily depends on expert-derived semantic descriptors defined by the Breast Imaging Reporting and Data System (BI-RADS) lexicon, which guide the interpretation of radiologists. This discrepancy raises a fundamental question regarding the relationship between explicit radiological domain knowledge and learned deep visual representations in breast cancer detection. In this work, we systematically investigate the impact of integrating BI-RADS descriptors into deep learning models for mammography-based breast cancer detection. We evaluate dedicated architectures designed to fuse BI-RADS descriptors with image features and compare them against image-only models and BI-RADS–only tree-based baselines. Our results show that incorporating BI-RADS descriptors consistently improves performance across lesion types and evaluation metrics. Notably, relatively simple models augmented with BI-RADS descriptors outperform substantially more complex image-only architectures. Moreover, BI-RADS integration enhances generalization, enabling a single unified model to effectively classify both mass and calcification lesions. These findings demonstrate that combining structured expert knowledge with deep learning yields more accurate, robust, and clinically aligned breast cancer detection systems.