Federated Learning on Distributed Medical Records for Detection of Lung Nodules

In this work, the concept of federated Learning is applied on medical records of CT scans images for detection of pulmonary lung nodules. Instead of using the naive ways, the authors have come up with decentralizing the training technique by bringing the model to the data rather than accumulating the data at a central place and thus maintaining differential privacy of the records. The training on distributed electronic medical records includes two models: detection of location of nodules and its confirmation. The experiments have been carried out on CT scan images from LIDC dataset and the results shows that the proposed method outperformed the existing methods in terms of detection accuracy.