Segmentation-Guided Attention Transfer Learning for Breast Cancer Classification in Digital Mammography

1. An annotation-free attention-weighting pipeline combining CLAHE and ROI extraction into a pixel-level input attention map;
2. A two-phase fine-tuning protocol across three architectures under a patient/exam-level, group-aware split, closing a leakage gap present in an earlier image-level evaluation; and
3. A quantitative validation of the generated attention mask against radiologist-drawn ROI annotations (Dice/IoU), complementing the qualitative Grad-CAM++ analysis.