11 citations · 31 across the 7 of their papers we have counts for
7 papers
ACE-LoRA: Graph-Attentive Context Enhancement for Parameter-Efficient Adaptation of Medical Vision-Language Models
M. Arda Aydın, Melih B. Yilmaz, Aykut Koç +1
The success of CLIP-like vision-language models (VLMs) on natural images has inspired medical counterparts, yet existing approaches largely fall into two extremes: specialist model…
CalibFPA: A Focal Plane Array Imaging System based on Online Deep-Learning Calibration
Alper Güngör, M. Umut Bahceci, Yasin Ergen +4
Compressive focal plane arrays (FPA) enable cost-effective high-resolution (HR) imaging by acquisition of several multiplexed measurements on a low-resolution (LR) sensor. Multiple…
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…
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…