Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation
Journal:
arXiv
Published Date:
Jun 29, 2025
Abstract
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.
