27 citations · 45 across the 8 of their papers we have counts for
8 papers
Prompt-Conditioned FiLM and Multi-Scale Fusion on MedSigLIP for Low-Dose CT Quality Assessment
Tolga Demiroglu, Mehmet Ozan Unal, Metin Ertas +1
We propose a prompt-conditioned framework built on MedSigLIP that injects textual priors via Feature-wise Linear Modulation (FiLM) and multi-scale pooling. Text prompts condition p…
Deep Unfolded BM3D: Unrolling Non-local Collaborative Filtering into a Trainable Neural Network
Kerem Basim, Mehmet Ozan Unal, Metin Ertas +1
Block-Matching and 3D Filtering (BM3D) exploits non-local self-similarity priors for denoising but relies on fixed parameters. Deep models such as U-Net are more flexible but often…
LMM-IQA: Image Quality Assessment for Low-Dose CT Imaging
Kagan Celik, Mehmet Ozan Unal, Metin Ertas +1
Low-dose computed tomography (CT) represents a significant improvement in patient safety through lower radiation doses, but increased noise, blur, and contrast loss can diminish di…
Task-Adaptive Low-Dose CT Reconstruction
Necati Sefercioglu, Mehmet Ozan Unal, Metin Ertas +1
Deep learning-based low-dose computed tomography reconstruction methods already achieve high performance on standard image quality metrics like peak signal-to-noise ratio and struc…
Learnable Total Variation with Lambda Mapping for Low-Dose CT Denoising
Yusuf Talha Basak, Mehmet Ozan Unal, Metin Ertas +1
While Total Variation (TV) excels in noise reduction and edge preservation, its reliance on a scalar regularization parameter limits adaptivity. In this study, we present a Learnab…
3D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutions
Doga Gunduzalp, Batuhan Cengiz, Mehmet Ozan Unal +1
In this paper, we introduced a novel deep learning-based reconstruction technique for low-dose CT imaging using 3 dimensional convolutions to include the sagittal information unlik…
An Unsupervised Reconstruction Method For Low-Dose CT Using Deep Generative Regularization Prior
Mehmet Ozan Unal, Metin Ertas, Isa Yildirim
Low-dose CT imaging requires reconstruction from noisy indirect measurements which can be defined as an ill-posed linear inverse problem. In addition to conventional FBP method in…
Self-Supervised Training For Low Dose CT Reconstruction
Mehmet Ozan Unal, Metin Ertas, Isa Yildirim
Ionizing radiation has been the biggest concern in CT imaging. To reduce the dose level without compromising the image quality, low-dose CT reconstruction has been offered with the…