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Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI
This challenge leads to necessitate extensive training data in many deep learning reconstruction methods.
Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation
For instance, balanced steady-state free precession cine sequences present clear anatomical boundaries, while late gadolinium enhancement and T2-weighted CMR sequences visualize myocardial scar and edema of MI, respectively.
MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images
We therefore conducted extensive experiments to investigate the performance of the proposed method in dealing with such complex combinations of different CMR sequences.
Learning Topic Models: Identifiability and Finite-Sample Analysis
Our theory introduces a new set of geometric conditions for topic model identifiability, conditions that are weaker than conventional separability conditions, which typically rely on the existence of pure topic documents or of anchor words.
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