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Found 5 papers, 3 papers with code
Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks
Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years.
What is the best data augmentation for 3D brain tumor segmentation?
Training segmentation networks requires large annotated datasets, which in medical imaging can be hard to obtain.
Vox2Vox: 3D-GAN for Brain Tumour Segmentation
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histological sub-regions, i. e., peritumoral edema, necrotic core, enhancing and non-enhancing tumour core.
Generating fMRI volumes from T1-weighted volumes using 3D CycleGAN
Registration between an fMRI volume and a T1-weighted volume is challenging, since fMRI volumes contain geometric distortions.
Refacing: reconstructing anonymized facial features using GANs
Anonymization of medical images is necessary for protecting the identity of the test subjects, and is therefore an essential step in data sharing.
