9 citations · 30 across the 9 of their papers we have counts for
11 papers
Stochastic Finite-Transition Learning and Exploration for Accelerated MRI Reconstruction
Muhammad Usama Mirza, Esin Öztürk Işık, Tolga Çukur
Generative MRI reconstruction maps an initial source distribution to the target image distribution along a learned recovery trajectory. Common generative methods, such as diffusion…
NeuroSSM: Multiscale Differential State-Space Modeling for Context-Aware fMRI Analysis
Furkan Genç, Boran İsmet Macun, Sait Sarper Özaslan +2
Accurate fMRI analysis requires sensitivity to temporal structure across multiple scales, as BOLD signals encode cognitive processes that emerge from fast transient dynamics to slo…
A Tutorial on MRI Reconstruction: From Modern Methods to Clinical Implications
Tolga Çukur, Salman U. H. Dar, Valiyeh Ansarian Nezhad +4
MRI is an indispensable clinical tool, offering a rich variety of tissue contrasts to support broad diagnostic and research applications. Clinical exams routinely acquire multiple…
Efficient Noise Calculation in Deep Learning-based MRI Reconstructions
Onat Dalmaz, Arjun D. Desai, Reinhard Heckel +3
Accelerated MRI reconstruction involves solving an ill-posed inverse problem where noise in acquired data propagates to the reconstructed images. Noise analyses are central to MRI…
Meta-Entity Driven Triplet Mining for Aligning Medical Vision-Language Models
Saban Ozturk, Melih B. Yilmaz, Muti Kara +3
Diagnostic imaging relies on interpreting both images and radiology reports, but the growing data volumes place significant pressure on medical experts, yielding increased errors a…
Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction
Valiyeh A. Nezhad, Gokberk Elmas, Bilal Kabas +3
While learning-based models hold great promise for MRI reconstruction, single-site models trained on limited local datasets often show poor generalization. This has motivated colla…
Physics-Driven Autoregressive State Space Models for Medical Image Reconstruction
Bilal Kabas, Fuat Arslan, Valiyeh A. Nezhad +3
Medical image reconstruction from undersampled acquisitions is an ill-posed inverse problem requiring accurate recovery of anatomical structures from incomplete measurements. Physi…
DenoMamba: A fused state-space model for low-dose CT denoising
Şaban Öztürk, Oğuz Can Duran, Tolga Çukur
Low-dose computed tomography (LDCT) lower potential risks linked to radiation exposure while relying on advanced denoising algorithms to maintain diagnostic quality in reconstructe…
Bayesian Conditioned Diffusion Models for Inverse Problems
Alper Güngör, Bahri Batuhan Bilecen, Tolga Çukur
Diffusion models have recently been shown to excel in many image reconstruction tasks that involve inverse problems based on a forward measurement operator. A common framework uses…
HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images
Şaban Öztürk, M. Yiğit Turalı, Tolga Çukur
Chest X-ray is an essential diagnostic tool in the identification of chest diseases given its high sensitivity to pathological abnormalities in the lungs. However, image-driven dia…
A plug-in graph neural network to boost temporal sensitivity in fMRI analysis
Irmak Sivgin, Hasan A. Bedel, Şaban Öztürk +1
Learning-based methods have recently enabled performance leaps in analysis of high-dimensional functional MRI (fMRI) time series. Deep learning models that receive as input functio…