Enhancing Federated Learning Performance for Medical Image Segmentation via Representation Learning
Published in Proceedings of the Korean Institute of Information Scientists and Engineers (KIISE) Conference, 2024
Abstract (excerpt). Privacy in the medical field is a paramount concern, especially when integrating artificial intelligence models into healthcare, as these models require substantial data to attain optimal accuracy. Indeed, federated learning emerges as a promising solution to train high-accuracy artificial intelligence models while safeguarding the privacy of patient data. However, given the inherent variability in patient demographics, equipment, and procedures, the original federated learning algorithm may not yield optimal performance when learning with data from many hospitals, referred to as an imbalance and non-iid (non-independent and identically distributed) data problem. In this paper, we propose FedRep, a method for addressing non-iid data in the medical field through representation learning. FedFeat is an experiment on the skin image dataset used for segmentation tasks. The experiments depict that our proposed …
Recommended citation: Phuong-Nam Tran, Hyeonsu Kim, Choong Seon Hong. (2024). "Enhancing Federated Learning Performance for Medical Image Segmentation via Representation Learning." Proceedings of the Korean Institute of Information Scientists and Engineers (KIISE) Conference, 794-796.
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