no code implementations • 23 Nov 2023 • Yigit Gurses, Melisa Taspinar, Mahmut Yurt, Sedat Ozer
On the other hand, our proposed solution, GRJointNet, is an architecture that can perform joint completion and segmentation on point clouds as a successor of GRNet.
3 code implementations • 6 Feb 2023 • Tiange Xiang, Mahmut Yurt, Ali B Syed, Kawin Setsompop, Akshay Chaudhari
Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique.
2 code implementations • 30 Jun 2021 • Onat Dalmaz, Mahmut Yurt, Tolga Çukur
Here, we propose a novel generative adversarial approach for medical image synthesis, ResViT, that leverages the contextual sensitivity of vision transformers along with the precision of convolution operators and realism of adversarial learning.}
Ranked #1 on Image-to-Image Translation on IXI
1 code implementation • 15 May 2021 • Yilmaz Korkmaz, Salman UH Dar, Mahmut Yurt, Muzaffer Özbey, Tolga Çukur
Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consistency.
no code implementations • 13 Mar 2021 • Salman Ul Hassan Dar, Mahmut Yurt, Tolga Çukur
Deep neural networks (DNNs) have recently found emerging use in accelerated MRI reconstruction.
no code implementations • 18 Dec 2020 • Muzaffer Özbey, Mahmut Yurt, Salman Ul Hassan Dar, Tolga Çukur
Mainstream deep models for three-dimensional MRI synthesis are either cross-sectional or volumetric depending on the input.
no code implementations • 29 Nov 2020 • Mahmut Yurt, Salman Ul Hassan Dar, Muzaffer Özbey, Berk Tınaz, Kader Karlı Oğuz, Tolga Çukur
Here, we propose a novel semi-supervised deep generative model that instead learns to recover high-quality target images directly from accelerated acquisitions of source and target contrasts.
no code implementations • 27 Nov 2020 • Mahmut Yurt, Muzaffer Özbey, Salman Ul Hassan Dar, Berk Tınaz, Kader Karlı Oğuz, Tolga Çukur
Comprehensive demonstrations on mainstream MRI reconstruction and synthesis tasks show that ProvoGAN yields superior performance to state-of-the-art volumetric and cross-sectional models.
no code implementations • 25 Sep 2019 • Mahmut Yurt, Salman Ul Hassan Dar, Aykut Erdem, Erkut Erdem, Tolga Çukur
Multi-contrast MRI protocols increase the level of morphological information available for diagnosis.
no code implementations • 27 May 2018 • Salman Ul Hassan Dar, Mahmut Yurt, Mohammad Shahdloo, Muhammed Emrullah Ildız, Tolga Çukur
The proposed method preserves high-frequency details of the target contrast by relying on the shared high-frequency information available from the source contrast, and prevents feature leakage or loss by relying on the undersampled acquisitions of the target contrast.
2 code implementations • 5 Feb 2018 • Salman Ul Hassan Dar, Mahmut Yurt, Levent Karacan, Aykut Erdem, Erkut Erdem, Tolga Çukur
The proposed approach preserves high-frequency details via an adversarial loss; and it offers enhanced synthesis performance via a pixel-wise loss for registered multi-contrast images and a cycle-consistency loss for unregistered images.