guopengf/FL-MRCM

Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning

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This project helps medical professionals reconstruct high-quality Magnetic Resonance (MR) images more quickly and accurately from under-sampled data. It takes raw MR scan data, potentially from multiple institutions, and outputs improved MR images by leveraging a privacy-preserving collaborative learning approach. Radiologists and medical researchers would use this to get better image clarity for diagnosis and study.

No commits in the last 6 months.

Use this if you need to improve the quality of MR image reconstruction by utilizing data from multiple medical institutions without compromising patient privacy or sharing raw data directly.

Not ideal if you are working with a single, large, centrally available dataset for training MR image reconstruction models, as the federated learning approach adds complexity not needed in that scenario.

medical-imaging radiology MRI-reconstruction clinical-diagnosis data-privacy-healthcare
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 17 / 25

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Stars

46

Forks

11

Language

Python

License

MIT

Last pushed

Jun 15, 2022

Commits (30d)

0

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