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
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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).Citations
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Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
Fei Shan,Yaozong Gao,Jun Wang,Weiya Shi,Nannan Shi,Miaofei Han,Zhong Xue,Dinggang Shen,Yuxin Shi +8 more
TL;DR: A deep learning (DL) based segmentation system is developed to automatically quantify infection regions of interest (ROIs) and their volumetric ratios w.r.t. the lung and possible applications, including but not limited to analysis of follow-up CT scans and infection distributions in the lobes and segments correlated with clinical findings were discussed.
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Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans
TL;DR: A novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT scans and outperforms most cutting-edge segmentation models and advances the state-of-the-art technology.
Journal ArticleDOI
A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization.
TL;DR: A cheap, fast, and reliable intelligence tool has been provided for COVID-19 infection detection, and the developed model can be used to assist field specialists, physicians, and radiologists in the decision-making process.
Journal ArticleDOI
Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection
Jianpeng Zhang,Yutong Xie,Guansong Pang,Zhibin Liao,Johan W. Verjans,Wenxing Li,Zongji Sun,Jian He,Yi Li,Chunhua Shen,Yong Xia +10 more
TL;DR: The proposed CAAD model outperforms binary classification models on the clinical X-VIRAL dataset that contains 5,977 viral pneumonia (no COVID-19) cases, 37,393 non-viral pneumonia or healthy cases and achieves an AUC of 83.61% and sensitivity of 71.70%, which is comparable to the performance of radiologists reported in the literature.
Journal ArticleDOI
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging
Rajesh Kumar,Abdullah Aman Khan,Jay Kumar,Zakria,Noorbakhsh Amiri Golilarz,Simin Zhang,Yang Ting,Chengyu Zheng,Wenyong Wang +8 more
TL;DR: A framework that collects a small amount of data from different sources and trains a global deep learning model using blockchain-based federated learning and uses Capsule Network-based segmentation and classification to detect COVID-19 patients and designs a method that can collaboratively train a global model using Blockchain technology with Federated learning while preserving privacy.
References
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