11 citations · 20 across the 4 of their papers we have counts for
4 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…
One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis
Onat Dalmaz, Usama Mirza, Gökberk Elmas +5
Multi-institutional collaborations are key for learning generalizable MRI synthesis models that translate source- onto target-contrast images. To facilitate collaboration, federate…
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…