Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation
This work addresses data scarcity and distribution issues in federated learning for medical imaging, specifically breast cancer diagnosis, but is incremental as it builds on existing FL methods with a focused enhancement.
The paper tackled the problem of limited and non-IID data degrading federated learning for breast cancer detection from ultrasound images by proposing a generative AI-based data augmentation framework with synthetic image sharing, which improved average AUC from 0.9206 to 0.9237 for FedAvg and from 0.9429 to 0.9538 for FedProx.
Federated learning (FL) has emerged as a promising paradigm for collaboratively training deep learning models across institutions without exchanging sensitive medical data. However, its effectiveness is often hindered by limited data availability and non-independent, identically distributed data across participating clients, which can degrade model performance and generalization. To address these challenges, we propose a generative AI based data augmentation framework that integrates synthetic image sharing into the federated training process for breast cancer diagnosis via ultrasound images. Specifically, we train two simple class-specific Deep Convolutional Generative Adversarial Networks: one for benign and one for malignant lesions. We then simulate a realistic FL setting using three publicly available breast ultrasound image datasets: BUSI, BUS-BRA, and UDIAT. FedAvg and FedProx are adopted as baseline FL algorithms. Experimental results show that incorporating a suitable number of synthetic images improved the average AUC from 0.9206 to 0.9237 for FedAvg and from 0.9429 to 0.9538 for FedProx. We also note that excessive use of synthetic data reduced performance, underscoring the importance of maintaining a balanced ratio of real and synthetic samples. Our findings highlight the potential of generative AI based data augmentation to enhance FL results in the breast ultrasound image classification task.