no code implementations • 3 May 2024 • Rucha Deshpande, Varun A. Kelkar, Dimitrios Gotsis, Prabhat KC, Rongping Zeng, Kyle J. Myers, Frank J. Brooks, Mark A. Anastasio
The goal of this challenge was to promote the development of deep generative models (DGMs) for medical imaging and to emphasize the need for their domain-relevant assessment via the analysis of relevant image statistics.
no code implementations • 16 Mar 2023 • Qing Lyu, Josh Tan, Michael E. Zapadka, Janardhana Ponnatapura, Chuang Niu, Kyle J. Myers, Ge Wang, Christopher T. Whitlow
The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities.
no code implementations • 26 Apr 2022 • Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks, Prabhat KC, Kyle J. Myers, Rongping Zeng, Mark A. Anastasio
In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoration, reconstruction, translation, as well as objective image quality assessment.
no code implementations • 7 Apr 2022 • Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks, Kyle J. Myers, Prabhat KC, Rongping Zeng, Mark A. Anastasio
However, procedures for establishing stochastic image models (SIMs) using GANs remain generic and do not address specific issues relevant to medical imaging.
no code implementations • 18 Nov 2021 • Prabhat KC, Rongping Zeng, M. Mehdi Farhangi, Kyle J. Myers
These metrics are employed to perform a more nuanced study of the resolution of the DNN outputs' low-contrast features, their noise textures, and their CT number accuracy to better understand the impact each DNN algorithm has on these underlying attributes of image quality.