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Found 3 papers, 2 papers with code
DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization
In recent years, the research landscape of machine learning in medical imaging has changed drastically from supervised to semi-, weakly- or unsupervised methods.
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
Training deep segmentation networks on texture-encoded input: application to neuroimaging of the developing neonatal brain
Standard practice for using convolutional neural networks (CNNs) in semantic segmentation tasks assumes that the image intensities are directly used for training and inference.
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