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Found 4 papers, 2 papers with code
Zero-Shot Physics-Guided Deep Learning for Subject-Specific MRI Reconstruction
In the presence of models pre-trained on a database, we show that the proposed approach can be adapted as subject-specific fine-tuning via transfer learning to further improve reconstruction quality.
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction
Both linear and non-linear methods are covered, followed by a discussion of recent efforts to further improve parallel imaging using machine learning, and specifically using artificial neural networks.
Sparse Phase Retrieval via Truncated Amplitude Flow
Upon formulating sparse PR as an amplitude-based nonconvex optimization task, SPARTA works iteratively in two stages: In stage one, the support of the underlying sparse signal is recovered using an analytically well-justified rule, and subsequently, a sparse orthogonality-promoting initialization is obtained via power iterations restricted on the support; and, in the second stage, the initialization is successively refined by means of hard thresholding based gradient-type iterations.
Information Theory Information Theory Optimization and Control
