Cross-Dataset Evaluation of the Lightweight YOLO Family for Breast Ultrasound Lesion Segmentation: Effects of Preprocessing, Hyperparameter Optimization, and Test-Time Augmentation

Abstract

Breast cancer remains a major global health concern, making accurate lesion assessment essential for effective clinical decision making. Deep learning has shown promising performance in breast ultrasound analysis, yet models evaluated on data from the same source may not generalize reliably to images acquired using different scanners and acquisition settings. This study therefore examines cross-dataset generalization and the factors that can improve it. Five lightweight YOLO instance-segmentation architectures (YOLOv8n, YOLO11n, YOLO11s, YOLO26n, and YOLO26s) were trained using a patient-grouped BUS-BRA protocol and a single training seed, and evaluated internally on held-out data and externally on BUS-UCLM, which served as the single target dataset. Ultrasound-specific preprocessing, test-time augmentation (TTA), and Optuna-selected configurations were assessed across 40 paired internal–external comparisons. Cross-dataset evaluation demonstrated consistent lesion delineation on BUS-UCLM, with matched Dice ranging from 0.861 to 0.875 and matched IoU from 0.764 to 0.786 across the five architectures. Preprocessing improved mask mAP@50–95 across all ten checkpoints on both datasets, while TTA improved detection-adjusted Dice despite having little effect on mAP@50–95. Optuna tuning improved external mAP@50–95 across all five architectures despite limited internal gains. Grad-CAM++ showed predominantly lesion-centered attention across both datasets, while ONNX Runtime deployment achieved 5.65–13.62 FPS on CPU. These findings highlight the importance of external validation, detection-aware evaluation, and efficient deployment for reliable breast ultrasound segmentation.