Summary of Cloud-based Federated Learning Framework For Mri Segmentation, by Rukesh Prajapati and Amr S. El-wakeel
Cloud-based Federated Learning Framework for MRI Segmentation
by Rukesh Prajapati, Amr S. El-Wakeel
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework for brain tissue segmentation in rural healthcare facilities combines deep reinforcement learning (DRL) with a refinement model (RM) and federated learning (FL) to optimize performance while respecting data privacy. The DRL environment is designed for local implementation at rural sites, reducing parameter counts and improving practicality. Federated learning enables cooperative training without compromising privacy constraints. Experimental results demonstrate substantial performance enhancements, achieving an average accuracy rate of 92% in rural healthcare settings with limited data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to help doctors analyze brain images from rural areas where there isn’t much data available. They created a special computer program that can learn and improve on its own by working together with other programs at different sites. This helps keep the patient information private while still getting good results. The program was tested and found to be very accurate, even when there wasn’t much data to work with. |
Keywords
* Artificial intelligence * Federated learning * Reinforcement learning
