Application of weak supervision in breast cancer detection using ultrasound images

Abstract

Breast cancer, the most frequently diagnosed cancer and the fifth leading cause of cancer-related deaths worldwide, underscores the importance of early detection for improved survival rates and treatment outcomes. The highly regarded mammography faces limitations in accessibility, par ticularly in resource-limited settings and younger patients with dense breast tissue. Ultrasound imaging emerges as a promising alternative due to its accessibility, cost-e!ectiveness, and real-time imaging capabilities. However, the manual interpretation of ultrasound images is challenging and prone to errors, highlighting need for automated methods. This research explored the application of weak supervision for the detection and classification of breast cancer using breast ultrasound images, comparing fully supervised (FS) and weakly super vised (WS) learning approaches. Models like YOLOv8, ResNet50, MobileNet, and VGG19 were trained and evaluated on public and local datasets. The weakly supervised models yielded com petitive and commendable results, nearly matching those of the fully supervised models. The ResNet50 model emerged as the top performer for both the FS and WS classification tasks. The fully supervised ResNet50 model achieved an AUC score of 64%, precision of 66.67%, and recall of 57.1%. The weakly supervised ResNet50 model achieved an AUC score of 56%, precision of 58.1%, and recall of 51.4%. The di!erence metrics were 8% higher in the AUC score, 8.6% higher in precision, and 5.74% higher in recall, but the performance di!erences were not statistically sig nificant. These weakly supervised performance metrics for the ResNet50 model outpaced those of other weakly supervised models and were either comparable to or marginally di!erent from the fully supervised results of YOLOv8, MobileNet, and VGG19. This highlighted weak supervision as a viable alternative when exhaustive annotations are impractical. This research also incorporated advanced visualization techniques like Grad-CAM to interpret model predictions, enhancing the understanding of model decision-making processes. Key limita tions included challenges in parameter optimization due to computational resource constraints and the insu”cient exploration of alternative weak supervision techniques. These approaches aimed to improve the robustness, accuracy, and applicability of deep learning models in breast cancer detection and classification