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Found 3 papers, 2 papers with code
Virtual embeddings and self-consistency for self-supervised learning
We validate TriMix on eight benchmark datasets consisting of natural and medical images with an improvement of 2. 71% and 0. 41% better than the second-best models for both data types.
Semi-Supervised Federated Peer Learning for Skin Lesion Classification
With few annotated data, FedPerl is on par with a state-of-the-art method in skin lesion classification in the standard setup while outperforming SSFLs and the baselines by 1. 8% and 15. 8%, respectively.
ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging
Medical image segmentation is one of the major challenges addressed by machine learning methods.
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