Region-of-Interest Augmentation for Mammography Classification under Patient-Level Cross-Validation

• URL: http://arxiv.org/abs/2509.20585v2
• Date: Sun, 05 Oct 2025 21:40:20 GMT
• Title: Region-of-Interest Augmentation for Mammography Classification under Patient-Level Cross-Validation
• Abstract summary: We revisit the Mini-DDSM dataset (9,684 images; 2,414 patients) and introduce a lightweight region-of-interest (ROI) augmentation strategy.<n>During training, full images are probabilistically replaced with random ROI crops sampled from a precomputed, label-free bounding-box bank.<n>We evaluate under strict patient-level cross-validation and report ROC-AUC, PR-AUC, and training-time efficiency metrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Breast cancer screening with mammography remains central to early detection and mortality reduction. Deep learning has shown strong potential for automating mammogram interpretation, yet limited-resolution datasets and small sample sizes continue to restrict performance. We revisit the Mini-DDSM dataset (9,684 images; 2,414 patients) and introduce a lightweight region-of-interest (ROI) augmentation strategy. During training, full images are probabilistically replaced with random ROI crops sampled from a precomputed, label-free bounding-box bank, with optional jitter to increase variability. We evaluate under strict patient-level cross-validation and report ROC-AUC, PR-AUC, and training-time efficiency metrics (throughput and GPU memory). Because ROI augmentation is training-only, inference-time cost remains unchanged. On Mini-DDSM, ROI augmentation (best: p_roi = 0.10, alpha = 0.10) yields modest average ROC-AUC gains, with performance varying across folds; PR-AUC is flat to slightly lower. These results demonstrate that simple, data-centric ROI strategies can enhance mammography classification in constrained settings without requiring additional labels or architectural modifications.

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