H
Huan Yuan
Publications - 8
Citations - 1147
Huan Yuan is an academic researcher. The author has contributed to research in topics: Feature selection & Overfitting. The author has an hindex of 7, co-authored 8 publications receiving 619 citations.
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Journal ArticleDOI
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia
Xi Ouyang,Jiayu Huo,Liming Xia,Fei Shan,Jun Liu,Zhanhao Mo,Fuhua Yan,Zhongxiang Ding,Qi Yang,Bin Song,Feng Shi,Huan Yuan,Ying Wei,Xiaohuan Cao,Yaozong Gao,Dijia Wu,Qian Wang,Dinggang Shen +17 more
TL;DR: Wang et al. as mentioned in this paper developed a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT), and proposed a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses.
Journal ArticleDOI
Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification
Feng Shi,Liming Xia,Fei Shan,Dijia Wu,Ying Wei,Huan Yuan,Huiting Jiang,Yaozong Gao,He Sui,Dinggang Shen +9 more
TL;DR: An infection size-aware random forest method (iSARF) was proposed for discriminating COVID-19 from CAP and yielded its best performance when using the handcrafted features, with a sensitivity and accuracy of 90.7%, a specificity and an accuracy of 89.4% over state-of-the-art classifiers.
Journal ArticleDOI
Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning
Hengyuan Kang,Liming Xia,Fuhua Yan,Zhibin Wan,Feng Shi,Huan Yuan,Huiting Jiang,Dijia Wu,He Sui,Changqing Zhang,Dinggang Shen +10 more
TL;DR: In this article, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability, while the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP).
Journal ArticleDOI
Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification.
Feng Shi,Liming Xia,Fei Shan,Bin Song,Dijia Wu,Ying Wei,Huan Yuan,Huiting Jiang,Yichu He,Yaozong Gao,He Sui,Dinggang Shen +11 more
TL;DR: In this article, a set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to conventional CT severity score (CT-SS) and Radiomics features.
Journal ArticleDOI
Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT
Liang Sun,Zhanhao Mo,Fuhua Yan,Liming Xia,Fei Shan,Zhongxiang Ding,Bin Song,Wanchun Gao,Wei Shao,Feng Shi,Huan Yuan,Huiting Jiang,Dijia Wu,Ying Wei,Yaozong Gao,He Sui,Daoqiang Zhang,Dinggang Shen +17 more
TL;DR: Experimental results on the CO VID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.