Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment


(Background) Federated learning is a decentralized approach to machine learning; it is a training strategy that overcomes medical data privacy regulations and generalizes deep learning algorithms. Federated learning mitigates many systemic privacy risks by sharing only the model and parameters for training, without the need to export existing medical data sets. In this study, we performed ultrasound image analysis using federated learning to predict whether thyroid nodules were benign or malignant. (Objective) The goal of this study was to evaluate whether the performance of federated learning was comparable with that of conventional deep learning. (Methods) A total of 8457 (5375 malignant, 3082 benign) ultrasound images were collected from 6 institutions and used for federated learning and conventional deep learning. Five deep learning networks (VGG19, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) were used. Using stratified random sampling, we selected 20% (1075 malignant, 616 benign) of the total images for internal validation. For external validation, we used 100 ultrasound images (50 malignant, 50 benign) from another institution.

JMIR Medical Informatics

연합학습 및 표현 학습을 사용한 medical 도메인에서의 딥 러닝 기법, 도메인 적응 및 테스트 타임 학습과 같은 딥 러닝 기반 컴퓨터 비전 응용, 이미지 처리 및 이미지-텍스트 캡셔닝을 포함한 의료 응용의 딥 러닝 기반 진단 등을 포함합니다.