Multi-Modal COVID-19 Discovery With Collaborative Federated Learning
Xiaomeng Chen, Yingxia Shao, Zhe Xue, Ziqiang Yu
Abstract
An effective and accurate method of detecting COVID-19 infection is to analyze medical diagnostic images (e.g. CT scans). However, patients’ information is privacy, and it is illegal to share diagnostic images among medical institutions. In this case, a critical issue faced by the model that detects the CT images is lacking enough training images dataset, then the features of COVID-19 cannot be accurately obtained. The data privacy attracts extensive attentions recently and is particularly important for the fast-developing medical institution database and. Considering this point, this paper presents a blockchain federated learning model, which overcomes the burden of centralized collection of large amounts of sensitive data. The model uses a trained model to recognize CT scans, and shares data between hospitals with privacy protection mechanism. This model is able to learn from shared resources or data between different hospital repositories to discover patients with new coronary pneumonia by detecting the computed tomography (CT) images. Finally, we conduct extensive experiments to verify the performance of the model.