Accuracy vs. Privacy: a Federated Learning Approach for Lung Ultrasound Pattern Classification

The exchange and flow of healthcare data are crucial and beneficial to develop automated methods for providing better care to patients. However, privacy is one of the major concerns when it comes to sharing medical data. Due to these concerns, limited data are available to develop such automated methods. To overcome this challenge, federated learning (FL), has gained significant interest within the research community as it allows to train deep learning (DL) models on different data sources without having to share data. In this study, our focus is on the evaluation of DL models trained to classify lung ultrasound (LUS) patterns in data acquired from multiple medical centers in FL setting. These patterns include horizontal artifacts, vertical artifacts, and small to large consolidations. To classify these patterns, we used ResNet-18 with spatial attention, trained and tested on 2104 LUS videos from 135 patients across 6 medical centers with a train-test split at the patient level. The cl...