DVF-YOLO-Seg: A two-stage breast mass segmentation model with enhanced feature extraction and small lesion detection.

Objective: Accurate segmentation of breast lesions, especially small ones, remains challenging in digital mammography due to complex anatomical structures and low-contrast boundaries. This study proposes DVF-YOLO-Seg, a two-stage segmentation framework designed to improve feature extraction and enhance small-lesion detection performance in mammographic images. Methods: The proposed method integrates an enhanced YOLOv10-based detection module with a segmentation stage based on the Visual Reference Prompt Segment Anything Model (VRP-SAM). A novel DualConv module is introduced to improve spatial, visual, and channel feature representation, while Varifocal Loss addresses class imbalance by emphasizing hard-to-detect lesions. The detection results are used to generate bounding box prompts for VRP-SAM, which refines the final lesion segmentation. Results: Experiments on the curated breast imaging subset of the digital database for screening mammography dataset demonstrate that DVF-YOLO-Seg achieves a precision of 79.7%, a recall of 81.5%, a dice coefficient of 80.2%, and an F1-score of 80.6%, outperforming baseline models. Particularly for lesions <5 mm, the model shows improved sensitivity. Ablation studies confirm the effectiveness of the DualConv module and Varifocal Loss. Additionally, the framework shows better visual consistency and clearer boundaries in clinician-evaluated results. Conclusion: DVF-YOLO-Seg significantly enhances the detection and segmentation accuracy for small breast lesions in mammography. By combining improved detection with prompt-based segmentation, this method offers a promising approach for computer-aided diagnosis in breast cancer screening.