2 citations · 2 across the 1 of their papers we have counts for
3 papers
Learning Deep MRI Reconstruction Models from Scratch in Low-Data Regimes
Salman UH Dar, Şaban Öztürk, Muzaffer Özbey +1
Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from prolonged scan times. Reconstruction methods can alleviate this limitation by recovering clinical…
Federated Learning of Generative Image Priors for MRI Reconstruction
Gokberk Elmas, Salman UH Dar, Yilmaz Korkmaz +5
Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit privacy risks arise during cross-site sharing of imaging data. Federated learning (FL)…
Semi-Supervised Learning of Mutually Accelerated MRI Synthesis without Fully-Sampled Ground Truths
Mahmut Yurt, Salman Ul Hassan Dar, Muzaffer Özbey +3
Learning-based synthetic multi-contrast MRI commonly involves deep models trained using high-quality images of source and target contrasts, regardless of whether source and target…