Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography

• URL: http://arxiv.org/abs/2507.06410v2
• Date: Thu, 10 Jul 2025 14:19:51 GMT
• Title: Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography
• Authors: Peyman Sharifian, Xiaotong Hong, Alireza Karimian, Mehdi Amini, Hossein Arabi,
• Abstract summary: This study proposes an automated deep learning system for robust binary classification of breast density.<n>We implemented and compared four advanced convolutional neural networks.<n>We developed a novel Combined Focal Label Smoothing Loss function that integrates focal loss, label smoothing, and class-balanced weighting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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