61 citations · 131 across the 3 of their papers we have counts for
3 papers
Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI
Syed Muhammad Anwar, Ismail Irmakci, Drew A. Torigian +5
Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and rese…
Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI
Burhaneddin Yaman, Hongyi Gu, Seyed Amir Hossein Hosseini +5
Self-supervised learning has shown great promise due to its capability to train deep learning MRI reconstruction methods without fully-sampled data. Current self-supervised learnin…
Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data
Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Steen Moeller +3
Deep learning (DL) has emerged as a tool for improving accelerated MRI reconstruction. A common strategy among DL methods is the physics-based approach, where a regularized iterati…