Coarse-to-Fine Curriculum Transfer Learning Using RF-Derived Ultrasound Representations for Small-Data Breast Tumor Detection.

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Abstract

Breast ultrasound (BUS) is important for breast tumor detection, but speckle noise, low contrast, operator dependency, and limited medical datasets hinder robust deep learning. Although raw radiofrequency (RF) signals contain richer acoustic information than conventional B-mode images, multimodal fusion approaches often increase computational cost. To address these issues, this study proposes a curriculum transfer learning-based approach that sequentially exploits different ultrasound information representations during training. The proposed approach maintains a single detection model architecture rather than relying on complex multimodal input fusion. Phase, Envelope, and B-mode images generated from raw RF signals were defined as distinct input domains, and various training orders were evaluated. In addition, lightweight detection models based on YOLOv5, YOLOv8, YOLO11, and YOLO26 were compared to select the optimal model. A total of nine experimental settings, including single-modality training and curriculum learning conditions, were repeatedly evaluated using 100 random seeds. The experimental results showed that the proposed Phase-Envelope-B-mode (P-E-B) curriculum transfer learning strategy achieved the highest average mAP@50 among the evaluated training scenarios, with an approximately 2.08% relative improvement over single B-mode training under the fixed patient-level split and 100-seed repeated evaluation setting. The average convergence epoch was also lower than that of single B-mode training, indicating that the proposed strategy provided a favorable convergence profile while improving average validation performance. These results should be interpreted as proof-of-concept evidence obtained under a fixed patient-level split and 100-seed repeated evaluation setting, rather than as conclusive evidence of external clinical generalizability. Within this controlled small-data setting, the findings suggest that RF-derived representations may provide useful training-stage curriculum information for B-mode-based breast tumor detection while maintaining a single B-mode inference pathway.

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