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Found 4 papers, 2 papers with code
FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging
To correct for respiratory motion in PET imaging, an interpretable and unsupervised deep learning technique, FlowNet-PET, was constructed.
Cycle-StarNet: Bridging the gap between theory and data by leveraging large datasets
To accomplish this, synthetic models are morphed into spectra that resemble observations, thereby reducing the gap between theory and observations.
Interpreting Stellar Spectra with Unsupervised Domain Adaptation
We discuss how to achieve mapping from large sets of imperfect simulations and observational data with unsupervised domain adaptation.
Reducing the Human Effort in Developing PET-CT Registration
We aim to reduce the tedious nature of developing and evaluating methods for aligning PET-CT scans from multiple patient visits.
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