Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
Breast density assessment is a crucial component of mammographic
interpretation, with high breast density (BI-RADS categories C and D)
representing both a significant risk factor for developing breast cancer and a
technical challenge for tumor detection. This study proposes an automated deep
learning system for robust binary classification of breast density (low: A/B
vs. high: C/D) using the VinDr-Mammo dataset. We implemented and compared four
advanced convolutional neural networks: ResNet18, ResNet50, EfficientNet-B0,
and DenseNet121, each enhanced with channel attention mechanisms. To address
the inherent class imbalance, we developed a novel Combined Focal Label
Smoothing Loss function that integrates focal loss, label smoothing, and
class-balanced weighting. Our preprocessing pipeline incorporated advanced
techniques, including contrast-limited adaptive histogram equalization (CLAHE)
and comprehensive data augmentation. The individual models were combined
through an optimized ensemble voting approach, achieving superior performance
(AUC: 0.963, F1-score: 0.952) compared to any single model. This system
demonstrates significant potential to standardize density assessments in
clinical practice, potentially improving screening efficiency and early cancer
detection rates while reducing inter-observer variability among radiologists.
