Federated learning using multi-institutional data for generalizable chest x-ray diagnosis

Dabbara Keshava Chowdari; Nunna Radhasyam; Anabik Pal; Angshuman Paul

doi:10.1117/12.2652553

7 April 2023 Federated learning using multi-institutional data for generalizable chest x-ray diagnosis

Dabbara Keshava Chowdari, Nunna Radhasyam, Anabik Pal, Angshuman Paul

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124650H (2023) https://doi.org/10.1117/12.2652553
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Dabbara Keshava Chowdari, Nunna Radhasyam, Anabik Pal, and Angshuman Paul "Federated learning using multi-institutional data for generalizable chest x-ray diagnosis", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124650H (7 April 2023); https://doi.org/10.1117/12.2652553

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