B
Bin Kong
Researcher at University of North Carolina at Charlotte
Publications - 33
Citations - 1976
Bin Kong is an academic researcher from University of North Carolina at Charlotte. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 8, co-authored 29 publications receiving 1214 citations. Previous affiliations of Bin Kong include Xi'an Jiaotong University & Chinese PLA General Hospital.
Papers
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Journal ArticleDOI
Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.
Lin Li,Lixin Qin,Zeguo Xu,Youbing Yin,Xin Wang,Bin Kong,Junjie Bai,Lu Yi,Zhenghan Fang,Qi Song,Kunlin Cao,Daliang Liu,Guisheng Wang,Qi-Zhong Xu,Xisheng Fang,Shiqin Zhang,Juan Xia,Jun Xia +17 more
TL;DR: A deep learning model was developed to extract visual features from volumetric chest CT scans for the detection of coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions.
Book ChapterDOI
Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network
TL;DR: A novel deep learning architecture is proposed, named as temporal regression network (TempReg-Net), to accurately identify specific frames from MRI sequences, by integrating the Convolutional Neural Network (CNN) with the Recurrent Neural network (RNN).
Book ChapterDOI
Cancer Metastasis Detection via Spatially Structured Deep Network
TL;DR: A novel deep neural network, namely Spatially Structured Network (Spatio-Net) is proposed to tackle the metastasis detection problem in WSIs by integrating the Convolutional Neural Network with the 2D Long-Short Term Memory (2D-LSTM).
Journal ArticleDOI
Learning tree-structured representation for 3D coronary artery segmentation.
TL;DR: A novel tree-structured convolutional gated recurrent unit (ConvGRU) model is proposed to learn the anatomical structure of the coronary artery, which considers the local spatial correlations in the input data as the convolutions are used for input-to-state as well as state- to-state transitions, thus more suitable for image analysis.
Book ChapterDOI
Invasive Cancer Detection Utilizing Compressed Convolutional Neural Network and Transfer Learning
TL;DR: This work proposes to detect invasive cancer employing a lightweight network in a fully convolution fashion without model ensembles, which requires less high performance computing resources than state-of-the-art methods, which makes the invasive cancer diagnosis more applicable in the clinical usage.