Z
Zhicheng Jiao
Researcher at University of Pennsylvania
Publications - 69
Citations - 1386
Zhicheng Jiao is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 11, co-authored 43 publications receiving 638 citations. Previous affiliations of Zhicheng Jiao include University of North Carolina at Chapel Hill & Brown University.
Papers
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A deep feature based framework for breast masses classification
TL;DR: A deep feature based framework for breast mass classification task that mainly contains a convolutional neural network (CNN) and a decision mechanism to better simulate the diagnostic procedure operated by doctors and achieved state-of-art performance.
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Deep Convolutional Neural Networks for mental load classification based on EEG data
TL;DR: Inspired by the recent breakthrough via deep recurrent convolutional neural networks (CNNs) on classifying mental load, improved CNNs methods for this task are proposed, which contain less parameters than state-of-the-art ones, making it be more competitive in further practical application.
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Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection
TL;DR: This result demonstrates the effectiveness of deep learning in preclinical Alzheimer’s disease diagnosis, based on the complex and high-dimensional voxel-wise spatiotemporal patterns of the resting-state brain functional connectomics.
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A parasitic metric learning net for breast mass classification based on mammography
TL;DR: The parameter updating strategy for the parasitic metric net may inspire a way of improving performance of a pre-trained CNNs model on particular medical image processing or other computer vision tasks.
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Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study.
Zhicheng Jiao,Ji Whae Choi,Kasey Halsey,Thi My Linh Tran,Ben Hsieh,Dongcui Wang,Feyisope R. Eweje,Robin Wang,Ken Chang,Jing Wu,Scott Collins,Thomas Yi,Andrew T. Delworth,Tao Liu,Terrance T. Healey,Shaolei Lu,Jianxin Wang,Xue Feng,Michael K. Atalay,Li Yang,Michael Feldman,Paul J. Zhang,Weihua Liao,Yong Fan,Harrison X. Bai +24 more
TL;DR: In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores.