Abstract
Deep learning models have achieved state-of-the-art in many challenging domains, whereas it is a data-hungry method. Collecting sensitive and labelled medical data sets is challenging and costly. Recently, federated learning has been used to train a model without sharing the data at a central place for a single task. We propose a novel Multi-task federated learning (MTFL) approach to utilize the data sets of various similar kinds of tasks. We used two binary class X-ray data sets: Pneumonia disease classification and TB disease classification. We compared MTFL with federated learning for a single task and CNN with data in one place. Results show that MTFL has achieved better specificity and accuracy than other models.
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Kumar, P., Sinha, I.K., Singh, K.P. (2023). MTFL: Multi-task Federated Learning for Classification of Healthcare X-Ray Images. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_43
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