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Found 3 papers, 1 papers with code
Joint Self-Supervised and Supervised Contrastive Learning for Multimodal MRI Data: Towards Predicting Abnormal Neurodevelopment
The integration of different imaging modalities, such as structural, diffusion tensor, and functional magnetic resonance imaging, with deep learning models has yielded promising outcomes in discerning phenotypic characteristics and enhancing disease diagnosis.
Learning Empirical Bregman Divergence for Uncertain Distance Representation
Deep metric learning techniques have been used for visual representation in various supervised and unsupervised learning tasks through learning embeddings of samples with deep networks.
A Novel Collaborative Self-Supervised Learning Method for Radiomic Data
We compared our proposed method with other state-of-the-art self-supervised learning methods on a simulation study and two independent datasets.
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