Zobrazeno 1 - 10
of 180
pro vyhledávání: '"Sung, Kyunghyun"'
Autor:
Hung, Alex Ling Yu, Zheng, Haoxin, Zhao, Kai, Pang, Kaifeng, Terzopoulos, Demetri, Sung, Kyunghyun
Current deep learning-based models typically analyze medical images in either 2D or 3D albeit disregarding volumetric information or suffering sub-optimal performance due to the anisotropic resolution of MR data. Furthermore, providing an accurate un
Externí odkaz:
http://arxiv.org/abs/2407.01146
Autor:
Hung, Alex Ling Yu, Zheng, Haoxin, Zhao, Kai, Du, Xiaoxi, Pang, Kaifeng, Miao, Qi, Raman, Steven S., Terzopoulos, Demetri, Sung, Kyunghyun
A large portion of volumetric medical data, especially magnetic resonance imaging (MRI) data, is anisotropic, as the through-plane resolution is typically much lower than the in-plane resolution. Both 3D and purely 2D deep learning-based segmentation
Externí odkaz:
http://arxiv.org/abs/2311.04942
Denoising diffusion probabilistic models (DDPMs) have achieved impressive performance on various image generation tasks, including image super-resolution. By learning to reverse the process of gradually diffusing the data distribution into Gaussian n
Externí odkaz:
http://arxiv.org/abs/2307.11926
Autor:
Hung, Alex Ling Yu, Zheng, Haoxin, Miao, Qi, Raman, Steven S., Terzopoulos, Demetri, Sung, Kyunghyun
Prostate cancer is the second leading cause of cancer death among men in the United States. The diagnosis of prostate MRI often relies on the accurate prostate zonal segmentation. However, state-of-the-art automatic segmentation methods often fail to
Externí odkaz:
http://arxiv.org/abs/2203.15163
Autor:
Meng, Qi, Del Rosario, Irish, Sung, Kyunghyun, Janzen, Carla, Devaskar, Sherin U., Carpenter, Catherine L., Ritz, Beate
Publikováno v:
In Placenta January 2024 145:72-79
Akademický článek
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Autor:
Lee, Brian, Janzen, Carla, Aliabadi, Arya R., Lei, Margarida Y.Y., Wu, Holden, Liu, Dapeng, Vangala, Sitaram S., Devaskar, Sherin U., Sung, Kyunghyun
Publikováno v:
In Placenta 7 September 2023 140:90-99
Autor:
Liu, Yongkai, Yang, Guang, Mirak, Sohrab Afshari, Hosseiny, Melina, Azadikhah, Afshin, Zhong, Xinran, Reiter, Robert E., Lee, Yeejin, Raman, Steven, Sung, Kyunghyun
Our main objective is to develop a novel deep learning-based algorithm for automatic segmentation of prostate zone and to evaluate the proposed algorithm on an additional independent testing data in comparison with inter-reader consistency between tw
Externí odkaz:
http://arxiv.org/abs/1911.00127
Autor:
Fu, Jie, Zhong, Xinran, Li, Ning, Van Dams, Ritchell, Lewis, John, Sung, Kyunghyun, Raldow, Ann C., Jin, Jing, Qi, X. Sharon
Publikováno v:
2020 Phys. Med. Biol
Radiomic features achieve promising results in cancer diagnosis, treatment response prediction, and survival prediction. Our goal is to compare the handcrafted (explicitly designed) and deep learning (DL)-based radiomic features extracted from pre-tr
Externí odkaz:
http://arxiv.org/abs/1909.04012
Akademický článek
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