MLI-lab/Robustness-CS
Measuring the robustness of compressive sensing methods (including deep-learning-based ones) for image reconstruction.
This project helps medical imaging researchers and practitioners evaluate how robust image reconstruction methods are, especially those using deep learning. It takes partially acquired MRI data or existing reconstructions and produces quantitative measurements of how well different techniques recover details under various challenging conditions, like adversarial noise or shifts in data distribution. Medical imaging scientists and radiologists who develop or use advanced MRI reconstruction algorithms would benefit from this work.
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Use this if you need to rigorously test the reliability and accuracy of image reconstruction algorithms, including deep learning models, for accelerated MRI under different types of perturbations and data variations.
Not ideal if you are looking for a tool to perform routine, robust image reconstruction without needing to analyze the underlying method's resilience to specific challenges.
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32
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5
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Jul 12, 2021
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