Journal Article

Efficient federated learning for pediatric pneumonia on chest X-ray classification

Wan, Jian, Shen, Yangyu, Huang, Jie, Wang, Haijiang, Pan, Zegang, Zhang, Lei

2024

Overview

According to the World Health Organization (WHO), pneumonia kills about 2 million children under the age of 5 every year. Traditional machine learning methods can be used to diagnose chest X-rays of pneumonia in children, but there is a privacy and security issue in centralizing the data for training. Federated learning prevents data privacy leakage by sharing only the model and not the data, and it has a wide range of application in the medical field. We use federated learning method for classification, which effectively protects data security. And for the data heterogeneity phenomenon existing in the actual scenario, which will seriously affect the classification effect, we propose a method based on two-end control variables. Specifically, based on the classical federated learning FedAvg algorithm, we modify the loss function on the client side by adding a regular term or a penalty term, and add momentum after the average aggregation on the server side. The federated learning approach prevents the data privacy leakage problem compared to the traditional machine learning approach. In order to solve the problem of low classification accuracy due to data heterogeneity, our proposed method based on two-end control variables achieves an average improvement of 2% and an accuracy of 98% on average, and 99% individually, compared to the previous federated learning algorithms and the latest diffusion model-based method. The classification results and methodology of this study can be utilized by clinicians worldwide to improve the overall detection of pediatric pneumonia.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 692/699/1785 / 692/700/1421/1770 / Algorithms / Chest / Chest X-ray classification of pediatric pneumonia / Child / Child, Preschool / Classification / Control variables at both ends / Data heterogeneity / Diffusion models / Federated learning / Heterogeneity / Humanities and Social Sciences / Humans / Infant / Learning algorithms / Machine Learning / multidisciplinary / Pediatrics / Pneumonia / Pneumonia - diagnosis / Pneumonia - diagnostic imaging / Privacy / Radiography, Thoracic - methods / Science / Science (multidisciplinary)