602 citations · 643 across the 8 of their papers we have counts for
10 papers
I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling
Omer F. Atli, Bilal Kabas, Fuat Arslan +4
Multi-modal medical image synthesis involves nonlinear transformation of tissue signals between source and target modalities, where tissues exhibit contextual interactions across d…
ResViT: Residual vision transformers for multi-modal medical image synthesis
Onat Dalmaz, Mahmut Yurt, Tolga Çukur
Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks. Howeve…
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers
Yilmaz Korkmaz, Salman UH Dar, Mahmut Yurt +2
Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding th…
A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors
Salman Ul Hassan Dar, Mahmut Yurt, Tolga Çukur
Deep neural networks (DNNs) have recently found emerging use in accelerated MRI reconstruction. DNNs typically learn data-driven priors from large datasets constituting pairs of un…
Three Dimensional MR Image Synthesis with Progressive Generative Adversarial Networks
Muzaffer Özbey, Mahmut Yurt, Salman Ul Hassan Dar +1
Mainstream deep models for three-dimensional MRI synthesis are either cross-sectional or volumetric depending on the input. Cross-sectional models can decrease the model complexity…
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…