cherise215/AdvBias
[MICCAI 2020 Oral] Realistic Adversarial Data Augmentation for MR Image Segmentation
This helps medical image analysts and researchers improve the accuracy of machine learning models used for segmenting anatomical structures in MR images. It takes raw or augmented MR images and synthetically adds realistic 'bias field' artifacts, which are common signal corruptions, to make models more robust. The primary users are researchers and practitioners developing or training AI for medical image analysis.
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Use this if you are training a neural network for medical image segmentation and need to make your model more resilient to realistic signal inconsistencies often found in MRI scans.
Not ideal if you are looking for general pixel-wise adversarial attacks or generating entirely new synthetic medical images from scratch.
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Jupyter Notebook
License
MIT
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Last pushed
Jul 05, 2022
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