J
Jihae Lee
Researcher at Tsinghua University
Publications - 4
Citations - 495
Jihae Lee is an academic researcher from Tsinghua University. The author has contributed to research in topics: The Internet & Feature selection. The author has an hindex of 3, co-authored 4 publications receiving 276 citations.
Papers
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Journal ArticleDOI
AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system.
Bo Wang,Shuo Jin,Qingsen Yan,Haibo Xu,Chuan Luo,Lai Wei,Wei Zhao,Hou Xuexue,Wenshuo Ma,Zhengqing Xu,Zhuozhao Zheng,Wenbo Sun,Lan Lan,Wei Zhang,Xiangdong Mu,Chenxi Shi,Zhong-Xiao Wang,Jihae Lee,Zijian Jin,Minggui Lin,Jin Hongbo,Liang Zhang,Jun Guo,Benqi Zhao,Zhizhong Ren,Shuhao Wang,Wei Xu,Xinghuan Wang,Jianming Wang,Jianming Wang,Zheng You,Jiahong Dong +31 more
TL;DR: An AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia and is able to overcome a series of challenges in this particular situation and deploy the system in four weeks.
Posted ContentDOI
AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks
Shuo Jin,Bo Wang,Haibo Xu,Chuan Luo,Lai Wei,Wei Zhao,Hou Xuexue,Wenshuo Ma,Zhengqing Xu,Zhuozhao Zheng,Wenbo Sun,Lan Lan,Zhang Wei,Xiangdong Mu,Chenxi Shi,Zhong-Xiao Wang,Jihae Lee,Zijian Jin,Minggui Lin,Jin Hongbo,Liang Zhang,Jun Guo,Benqi Zhao,Zhizhong Ren,Shuhao Wang,Zheng You,Jiahong Dong,Xinghuan Wang,Jianming Wang,Jianming Wang,Wei Xu +30 more
TL;DR: An AI system that automatically analyzes CT images to detect COVID-19 pneumonia features and was able to overcome a series of challenges in this particular situation and deploy the system in four weeks.
Proceedings ArticleDOI
FraudVis: Understanding Unsupervised Fraud Detection Algorithms
TL;DR: This work proposes a visualization system, FraudVis, to visually analyze the unsupervised fraud detection algorithms from temporal, intra- group correlation, inter-group correlation, feature selection, and the individual user perspectives, and helps domain experts better understand the algorithm output and the detected fraud behaviors.
Proceedings ArticleDOI
FDHelper: Assist Unsupervised Fraud Detection Experts with Interactive Feature Selection and Evaluation
TL;DR: This work designs and implements an end-to-end interactive visualization system, FDHelper, based on the deep understanding of the mechanism of the black market and fraud detection algorithms, and identifies a workflow based on experience from both fraud detection algorithm experts and domain experts.