Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation

2025/06/29 by Pan, Hongyi, Hong, Ziliang, Durak, Gorkem +2
#Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2506.23334

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.

Citations

• A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis
• IPMN Risk Assessment under Federated Learning Paradigm
• Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI
• Frequency-Based Federated Domain Generalization for Polyp Segmentation
• DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model
• Domain Generalization with Fourier Transform and Soft Thresholding
• DIGEST: Fast and Communication Efficient Decentralized Learning with Local Updates
• Adding Conditional Control to Text-to-Image Diffusion Models
• Adding Conditional Control to Text-to-Image Diffusion Models
• Multi-Contrast MRI Segmentation Trained on Synthetic Images
• Federated Momentum Contrastive Clustering
• High-Resolution Image Synthesis with Latent Diffusion Models
• High-Resolution Image Synthesis with Latent Diffusion Models
• Breast Cancer—Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies—An Updated Review
• FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
• FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
• Denoising Diffusion Probabilistic Models
• Federated Optimization in Heterogeneous Networks
• A Style-Based Generator Architecture for Generative Adversarial Networks
• Densely Connected Convolutional Networks
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

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• Knowledge-driven AI-generated data for accurate and interpretable breast ultrasound diagnoses
• Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study