Zobrazeno 1 - 10
of 437
pro vyhledávání: '"Pham, Dzung L."'
Autor:
Bian, Zhangxing, Alshareef, Ahmed, Wei, Shuwen, Chen, Junyu, Wang, Yuli, Woo, Jonghye, Pham, Dzung L., Zhuo, Jiachen, Carass, Aaron, Prince, Jerry L.
Magnetic Resonance Imaging with tagging (tMRI) has long been utilized for quantifying tissue motion and strain during deformation. However, a phenomenon known as tag fading, a gradual decrease in tag visibility over time, often complicates post-proce
Externí odkaz:
http://arxiv.org/abs/2401.17571
Autor:
Zhang, Jinwei, Zuo, Lianrui, Dewey, Blake E., Remedios, Samuel W., Pham, Dzung L., Carass, Aaron, Prince, Jerry L.
Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation. Current state-of-the-art automatic MS lesion segmentation metho
Externí odkaz:
http://arxiv.org/abs/2312.01460
Autor:
Zhang, Jinwei, Zuo, Lianrui, Dewey, Blake E., Remedios, Samuel W., Hays, Savannah P., Pham, Dzung L., Prince, Jerry L., Carass, Aaron
Deep learning algorithms utilizing magnetic resonance (MR) images have demonstrated cutting-edge proficiency in autonomously segmenting multiple sclerosis (MS) lesions. Despite their achievements, these algorithms may struggle to extend their perform
Externí odkaz:
http://arxiv.org/abs/2310.20586
Autor:
Remedios, Samuel W., Han, Shuo, Xue, Yuan, Carass, Aaron, Tran, Trac D., Pham, Dzung L., Prince, Jerry L.
In 2D multi-slice magnetic resonance (MR) acquisition, the through-plane signals are typically of lower resolution than the in-plane signals. While contemporary super-resolution (SR) methods aim to recover the underlying high-resolution volume, the e
Externí odkaz:
http://arxiv.org/abs/2209.02611
Autor:
Carass, Aaron, Greenman, Danielle, Dewey, Blake E., Calabresi, Peter A., Prince, Jerry L., Pham, Dzung L.
Publikováno v:
In Neuroimage: Reports March 2024 4(1)
Autor:
Pham, Dzung L., Chou, Yi-Yu, Dewey, Blake E., Reich, Daniel S., Butman, John A., Roy, Snehashis
Deep learning approaches to the segmentation of magnetic resonance images have shown significant promise in automating the quantitative analysis of brain images. However, a continuing challenge has been its sensitivity to the variability of acquisiti
Externí odkaz:
http://arxiv.org/abs/2103.02767
Autor:
Remedios, Samuel W., Wu, Zihao, Bermudez, Camilo, Kerley, Cailey I., Roy, Snehashis, Patel, Mayur B., Butman, John A., Landman, Bennett A., Pham, Dzung L.
Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to contain multiple instances. MIL is gaining traction for learning from weak
Externí odkaz:
http://arxiv.org/abs/1911.05650
Autor:
Remedios, Samuel, Roy, Snehashis, Blaber, Justin, Bermudez, Camilo, Nath, Vishwesh, Patel, Mayur B., Butman, John A., Landman, Bennett A., Pham, Dzung L.
Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data. However, to preserve patient anonymity it is frequently impractical or prohibited to transfer prote
Externí odkaz:
http://arxiv.org/abs/1903.04207
Autor:
Pham, Dzung L., Roy, Snehashis
A key feature of magnetic resonance (MR) imaging is its ability to manipulate how the intrinsic tissue parameters of the anatomy ultimately contribute to the contrast properties of the final, acquired image. This flexibility, however, can lead to sub
Externí odkaz:
http://arxiv.org/abs/1811.07087
Autor:
Knutsen, Andrew K., Vidhate, Suhas, McIlvain, Grace, Luster, Josh, Galindo, Eric J., Johnson, Curtis L., Pham, Dzung L., Butman, John A., Mejia-Alvarez, Ricardo, Tartis, Michaelann, Willis, Adam M.
Publikováno v:
In Journal of the Mechanical Behavior of Biomedical Materials February 2023 138