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

ABSTRACT:

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 feder

SUBMITTER: Lee H 

PROVIDER: S-EPMC8170555 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature