KEYWORDS: Data modeling, Chest imaging, Education and training, Machine learning, Performance modeling, Deep learning, Systems modeling, Design and modelling, Data privacy, Statistical modeling, Medical imaging, Computer aided detection
Deep learning models have achieved great success for the automated analysis of chest x-rays. However, many such models lack generalizability, i.e., a model trained in one dataset often performs poorly in a different dataset. One possible reason of such performance drop is the difference in the distribution of data from different institutions. In this context, utilization of data from multiple institutions to train a deep learning model may be helpful towards including a wider variety of data during training. This can improve the generalizability of the trained model. However, such an approach do not to preserve data privacy. To deal with the aforementioned limitation, federated learning may be useful. Federated learning allows multiple institutions to develop a machine learning model utilizing data from all institutions without sharing the data. Thus, federated learning approaches help in preserving data privacy. Although there has been a significant advancement in federated learning, such methods are rare in the context of chest x-ray diagnosis. Furthermore, most of such models do not utilize chest x-ray datasets from multiple institutions. In this work, we design a federated learning framework for chest x-ray diagnosis using datasets from multiple institutions. Our model shows improved generalizability in chest x-ray diagnosis across several publicly available large-scale chest x-ray datasets.
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