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pro vyhledávání: '"Dar, Salman Ul Hassan"'
Contemporary developments in generative AI are rapidly transforming the field of medical AI. These developments have been predominantly driven by the availability of large datasets and high computing power, which have facilitated a significant increa
Externí odkaz:
http://arxiv.org/abs/2407.14892
Autor:
Dar, Salman Ul Hassan, Seyfarth, Marvin, Kahmann, Jannik, Ayx, Isabelle, Papavassiliu, Theano, Schoenberg, Stefan O., Frey, Norbert, Baeßler, Bettina, Foersch, Sebastian, Truhn, Daniel, Kather, Jakob Nikolas, Engelhardt, Sandy
AI models present a wide range of applications in the field of medicine. However, achieving optimal performance requires access to extensive healthcare data, which is often not readily available. Furthermore, the imperative to preserve patient privac
Externí odkaz:
http://arxiv.org/abs/2402.01054
Autor:
Dar, Salman Ul Hassan, Ghanaat, Arman, Kahmann, Jannik, Ayx, Isabelle, Papavassiliu, Theano, Schoenberg, Stefan O., Engelhardt, Sandy
Generative latent diffusion models have been established as state-of-the-art in data generation. One promising application is generation of realistic synthetic medical imaging data for open data sharing without compromising patient privacy. Despite t
Externí odkaz:
http://arxiv.org/abs/2307.01148
Deep-learning models have enabled performance leaps in analysis of high-dimensional functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for contextual representations across diverse time scales. Here, we present BolT, a
Externí odkaz:
http://arxiv.org/abs/2205.11578
A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors
Deep neural networks (DNNs) have recently found emerging use in accelerated MRI reconstruction. DNNs typically learn data-driven priors from large datasets constituting pairs of undersampled and fully-sampled acquisitions. Acquiring such large datase
Externí odkaz:
http://arxiv.org/abs/2103.07790
Mainstream deep models for three-dimensional MRI synthesis are either cross-sectional or volumetric depending on the input. Cross-sectional models can decrease the model complexity, but they may lead to discontinuity artifacts. On the other hand, vol
Externí odkaz:
http://arxiv.org/abs/2101.05218
Autor:
Yurt, Mahmut, Dar, Salman Ul Hassan, Özbey, Muzaffer, Tınaz, Berk, Oğuz, Kader Karlı, Çukur, Tolga
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 domain samples are paired or unpaired. This results in undesirable rel
Externí odkaz:
http://arxiv.org/abs/2011.14347
Autor:
Yurt, Mahmut, Özbey, Muzaffer, Dar, Salman Ul Hassan, Tınaz, Berk, Oğuz, Kader Karlı, Çukur, Tolga
Magnetic resonance imaging (MRI) offers the flexibility to image a given anatomic volume under a multitude of tissue contrasts. Yet, scan time considerations put stringent limits on the quality and diversity of MRI data. The gold-standard approach to
Externí odkaz:
http://arxiv.org/abs/2011.13913
Publikováno v:
In Computers in Biology and Medicine December 2023 167
Multi-contrast MRI protocols increase the level of morphological information available for diagnosis. Yet, the number and quality of contrasts is limited in practice by various factors including scan time and patient motion. Synthesis of missing or c
Externí odkaz:
http://arxiv.org/abs/1909.11504