241 citations · 258 across the 10 of their papers we have counts for
10 papers
Next-Acceleration-Scale Prediction for Autoregressive MRI Reconstruction
Yilmaz Korkmaz, Vishal M. Patel
MRI reconstruction is an inherently ill-posed inverse problem, since incomplete measurements admit many plausible solutions. This ambiguity becomes more severe under high accelerat…
RemoteVAR: Autoregressive Visual Modeling for Remote Sensing Change Detection
Yilmaz Korkmaz, Vishal M. Patel
Remote sensing change detection aims to localize and characterize scene changes between two time points and is central to applications such as environmental monitoring and disaster…
Referring Change Detection in Remote Sensing Imagery
Yilmaz Korkmaz, Jay N. Paranjape, Celso M. de Melo +1
Change detection in remote sensing imagery is essential for applications such as urban planning, environmental monitoring, and disaster management. Traditional change detection met…
MambaRecon: MRI Reconstruction with Structured State Space Models
Yilmaz Korkmaz, Vishal M. Patel
Magnetic Resonance Imaging (MRI) is one of the most important medical imaging modalities as it provides superior resolution of soft tissues, albeit with a notable limitation in sca…
I2I-Galip: Unsupervised Medical Image Translation Using Generative Adversarial CLIP
Yilmaz Korkmaz, Vishal M. Patel
Unpaired image-to-image translation is a challenging task due to the absence of paired examples, which complicates learning the complex mappings between the distinct distributions…
Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction
Muhammad U. Mirza, Onat Dalmaz, Hasan A. Bedel +5
Deep generative models have gained recent traction in accelerated MRI reconstruction. Diffusion priors are particularly promising given their representational fidelity. Instead of…
Self-Supervised MRI Reconstruction with Unrolled Diffusion Models
Yilmaz Korkmaz, Tolga Cukur, Vishal M. Patel
Magnetic Resonance Imaging (MRI) produces excellent soft tissue contrast, albeit it is an inherently slow imaging modality. Promising deep learning methods have recently been propo…
Adaptive Diffusion Priors for Accelerated MRI Reconstruction
Alper Güngör, Salman UH Dar, Şaban Öztürk +4
Deep MRI reconstruction is commonly performed with conditional models that de-alias undersampled acquisitions to recover images consistent with fully-sampled data. Since conditiona…
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)…
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…