1-3 of 3
Harrison X. Bai
Close
Save search
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Journal Articles

A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images

Dan Cheng, Zhizheng Zhuo, Jiang Du, Jinyuan Weng, Chengzhou Zhang, Yunyun Duan, Ting Sun, Minghao Wu, Min Guo, Tiantian Hua, Ying Jin, Boyang Peng, Zhaohui Li, Mingwang Zhu, Maliha Imami, Chetan Bettegowda, Haris Sair, Harrison X. Bai, Frederik Barkhof, Xing Liu, Yaou Liu
Journal: Clinical Cancer Research
Clin Cancer Res (2024) 30 (1): 150–158.
https://doi.org/10.1158/1078-0432.CCR-23-1461
Published: 05 January 2024
View articletitled, A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images
Open the PDF for A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images in another window
Includes: Supplementary data
Journal Articles

Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging

Ianto Lin Xi, Yijun Zhao, Robin Wang, Marcello Chang, Subhanik Purkayastha, Ken Chang, Raymond Y. Huang, Alvin C. Silva, Martin Vallières, Peiman Habibollahi, Yong Fan, Beiji Zou, Terence P. Gade, Paul J. Zhang, Michael C. Soulen, Zishu Zhang, Harrison X. Bai, S. William Stavropoulos
Journal: Clinical Cancer Research
Clin Cancer Res (2020) 26 (8): 1944–1952.
https://doi.org/10.1158/1078-0432.CCR-19-0374
Published: 15 April 2020
View articletitled, Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging
Open the PDF for Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging in another window
Includes: Supplementary data
Journal Articles

Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging

Ken Chang, Harrison X. Bai, Hao Zhou, Chang Su, Wenya Linda Bi, Ena Agbodza, Vasileios K. Kavouridis, Joeky T. Senders, Alessandro Boaro, Andrew Beers, Biqi Zhang, Alexandra Capellini, Weihua Liao, Qin Shen, Xuejun Li, Bo Xiao, Jane Cryan, Shakti Ramkissoon, Lori Ramkissoon, Keith Ligon, Patrick Y. Wen, Ranjit S. Bindra, John Woo, Omar Arnaout, Elizabeth R. Gerstner, Paul J. Zhang, Bruce R. Rosen, Li Yang, Raymond Y. Huang, Jayashree Kalpathy-Cramer
Journal: Clinical Cancer Research
Clin Cancer Res (2018) 24 (5): 1073–1081.
https://doi.org/10.1158/1078-0432.CCR-17-2236
Published: 28 February 2018
View articletitled, Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging
Open the PDF for Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging in another window
Includes: Supplementary data