Huiling Wu
Wuhan University
12 Papers
Huiling Wu is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Colonoscopy. The author has an hindex of 3, co-authored 5 publications.
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Papers
Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography
Jun Chen,Lianlian Wu,Jun Zhang,Liang Zhang,Dexin Gong,Yilin Zhao,Qiuxiang Chen,Shulan Huang,Ming Yang,Xiao Yang,Shan Hu,Yonggui Wang,Xiao Hu,Biqing Zheng,Kuo Zhang,Huiling Wu,Zehua Dong,Youming Xu,Yijie Zhu,Xi Chen,Mengjiao Zhang,Lilei Yu,Fan Cheng,Honggang Yu +23 more
TL;DR: The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice, to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT.
Deep learning–based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video)
Jun Zhang,Liangru Zhu,Liwen Yao,Xiangwu Ding,Di Chen,Huiling Wu,Zihua Lu,Wei Zhou,Lihui Zhang,Ping An,Bo Xu,Wei Tan,Shan Hu,Fan Cheng,Honggang Yu +14 more
TL;DR: A system named Pancreaticobiliary (BP) master for EUS training and quality control has potential to play an important role in shortening the pancreatic EUS learning curve and improving EUS quality control in the future.
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A deep learning-based system for bile duct annotation and station recognition in linear endoscopic ultrasound.
Liwen Yao,Jun Zhang,Jun Liu,Liangru Zhu,Xiangwu Ding,Di Chen,Huiling Wu,Zihua Lu,Wei Zhou,Lihui Zhang,Bo Xu,Shan Hu,Biqing Zheng,Yanning Yang,Honggang Yu +14 more
TL;DR: Wang et al. as mentioned in this paper developed a deep learning-based augmentation system for endoscopic bile duct (BD) scanning augmentation, which achieved an accuracy of 93.3% in image set and 90.1% in video set.
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Development and validation of an artificial intelligence‐based system for predicting colorectal cancer invasion depth using multi‐modal data
Liwen Yao,Zihua Lu,Genhuan Yang,Wei Zhou,Youming Xu,Mingwen Guo,Xu Huang,Chunping He,Rui Zhou,Yunchao Deng,Huiling Wu,Bo Chen,R. Gong,Lihui Zhang,Mengjiao Zhang,Wei Gong,Hong-Cheung Jeffrey Yu +16 more
TL;DR: In this paper , an Artificial Intelligence (AI) system was used for the identification of presence of cancer invasion in large sessile colorectal polyps, where the authors aimed to construct a clinically applicable artificial intelligence system.
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Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart
TL;DR: In this article , an effective artificial intelligence recognition model is established by combining ultrasound images with artificial intelligence technology to assist ultrasound doctors in prenatal ultrasound fetal heart standard section recognition, which can provide an auxiliary diagnostic basis for ultrasound doctors to scan and lay a solid foundation for the diagnosis of congenital heart disease.
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