no code implementations • 5 Jul 2024 • Mahdi Ait Lhaj Loutfi, Teodora Boblea Podasca, Alex Zwanenburg, Taman Upadhaya, Jorge Barrios, David R. Raleigh, William C. Chen, Dante P. I. Capaldi, Hong Zheng, Olivier Gevaert, Jing Wu, Alvin C. Silva, Paul J. Zhang, Harrison X. Bai, Jan Seuntjens, Steffen Löck, Patrick O. Richard, Olivier Morin, Caroline Reinhold, Martin Lepage, Martin Vallières
Purpose: Develop a methodology and tools to identify and explain the smallest set of predictive radiomic features.
no code implementations • 3 Jun 2024 • Zhusi Zhong, Helen Zhang, Fayez H. Fayad, Andrew C. Lancaster, John Sollee, Shreyas Kulkarni, Cheng Ting Lin, Jie Li, Xinbo Gao, Scott Collins, Colin Greineder, Sun H. Ahn, Harrison X. Bai, Zhicheng Jiao, Michael K. Atalay
Imaging features and/or clinical variables were then incorporated into DL models to predict survival outcomes.
1 code implementation • 19 Nov 2023 • Ruxiao Duan, Brian Caffo, Harrison X. Bai, Haris I. Sair, Craig Jones
Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning.
1 code implementation • RSNA 2020 • Harrison X. Bai, Robin Wang, Zeng Xiong, Ben Hsieh, Ken Chang, Kasey Halsey, Thi My Linh Tran, Ji Whae Choi, Dong-Cui Wang, Lin-Bo Shi, Ji Mei, Xiao-Long Jiang, Ian Pan, Qiu-Hua Zeng, Ping-Feng Hu, Yi-Hui Li, Fei-Xian Fu, Raymond Y. Huang, Ronnie Sebro, Qi-Zhi Yu, Michael K. Atalay, Wei-Hua Liao
Summary AI assistance improved radiologists’ performance in distinguishing COVID-19 from pneumonia of other etiology on chest CT.