Cloud-based Federated Learning Framework for MRI Segmentation

• URL: http://arxiv.org/abs/2403.00254v1
• Date: Fri, 1 Mar 2024 03:39:17 GMT
• Title: Cloud-based Federated Learning Framework for MRI Segmentation
• Authors: Rukesh Prajapati and Amr S. El-Wakeel
• Abstract summary: This paper proposes a novel framework tailored for brain tissue segmentation in rural healthcare facilities. The framework employs a deep reinforcement learning environment in tandem with a refinement model (RM) deployed locally at rural healthcare sites. We demonstrate the efficacy of our approach by training the network with a limited data set and observing a substantial performance enhancement.
• Score: 0.10878040851637999
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In contemporary rural healthcare settings, the principal challenge in diagnosing brain images is the scarcity of available data, given that most of the existing deep learning models demand extensive training data to optimize their performance, necessitating centralized processing methods that potentially compromise data privacy. This paper proposes a novel framework tailored for brain tissue segmentation in rural healthcare facilities. The framework employs a deep reinforcement learning (DRL) environment in tandem with a refinement model (RM) deployed locally at rural healthcare sites. The proposed DRL model has a reduced parameter count and practicality for implementation across distributed rural sites. To uphold data privacy and enhance model generalization without transgressing privacy constraints, we employ federated learning (FL) for cooperative model training. We demonstrate the efficacy of our approach by training the network with a limited data set and observing a substantial performance enhancement, mitigating inaccuracies and irregularities in segmentation across diverse sites. Remarkably, the DRL model attains an accuracy of up to 80%, surpassing the capabilities of conventional convolutional neural networks when confronted with data insufficiency. Incorporating our RM results in an additional accuracy improvement of at least 10%, while FL contributes to a further accuracy enhancement of up to 5%. Collectively, the framework achieves an average 92% accuracy rate within rural healthcare settings characterized by data constraints.

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