At the dated check, the references listed below either did not resolve in
Crossref or DataCite, or carried a retraction notice. Each one is shown with the
registry record that put it there.
The 67 checked references that resolve
resolves10.1016/j.ijsu.2020.02.034World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)
resolves10.1016/S2213-2600(20)30079-5Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study
resolves10.1148/radiol.2020200241Chest CT Findings in 2019 Novel Coronavirus (2019-nCoV) Infections from Wuhan, China: Key Points for the Radiologist
resolves10.1148/radiol.2020200463Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection
resolves10.1148/ryct.2020200034Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review
resolves10.1007/978-3-030-32245-8_64A Multi-modality Network for Cardiomyopathy Death Risk Prediction with CMR Images and Clinical Information
resolves10.3390/app8101715Visualization and Interpretation of Convolutional Neural Network Predictions in Detecting Pneumonia in Pediatric Chest Radiographs
resolves10.1097/RLI.0000000000000127Automated Classification of Usual Interstitial Pneumonia Using Regional Volumetric Texture Analysis in High-Resolution Computed Tomography
resolves10.1109/TMI.2016.2535865Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network
resolves10.1001/jama.2015.3656Preferred Reporting Items for a Systematic Review and Meta-analysis of Individual Participant Data
resolves10.1016/j.maturitas.2015.03.009Risk prediction in the community: A systematic review of case-finding instruments that predict adverse healthcare outcomes in community-dwelling older adults
resolves10.7326/m18-1377PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration
resolves10.1148/radiol.2020201491Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT
resolves10.1109/TMI.2020.2992546Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning
resolves10.1148/radiol.2020201874COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System
resolves10.3390/sym12040651Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning
resolves10.1016/j.compbiomed.2020.103795Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks
resolves10.1016/j.mehy.2020.109761COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images
resolves10.1016/j.cell.2020.04.045Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography
resolves10.1016/j.ejrad.2020.109041Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study
resolves10.1016/j.compbiomed.2020.103805COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
resolves10.1148/radiol.2020200905Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy
resolves10.1007/s40846-020-00529-4Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases
resolves10.21037/atm.2020.03.132Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study
resolves10.1109/TMI.2020.2995965A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT
resolves10.1109/TMI.2020.2994908Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images
resolves10.1109/TMI.2020.2994459Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound
resolves10.1109/JBHI.2019.2936151Localizing B-Lines in Lung Ultrasonography by Weakly Supervised Deep Learning, <i>In-Vivo</i> Results
resolves10.1038/s41598-020-76282-0Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography
resolves10.1016/j.asoc.2020.106897AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system
resolves10.1007/s10489-020-02051-1A deep learning system that generates quantitative CT reports for diagnosing pulmonary Tuberculosis
resolves10.21037/atm-20-3026Machine learning-based CT radiomics method for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study
resolves10.1016/j.chemolab.2020.104054An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image
resolves10.1007/s10096-020-03901-zClassification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks
resolves10.1109/CVPR.2017.369ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
resolves10.1016/S0140-6736(20)30211-7Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study
The 11 references without a DOI — listed, not checked
no DOI — not checkedCoronavirus disease (COVID-19) Weekly Epidemiological Update and Weekly Operational UpdateWorld Health Organization2021-03-30https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports
no DOI — not checkedRolling updates on coronavirus disease (COVID-19)World Health Organization2021-03-30https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen
no DOI — not checkedWorld Health Organization2021-03-30https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance
no DOI — not checkedSharing research data and findings relevant to the novel coronavirus (COVID-19) outbreakWellcome Trust202001312021-03-31https://wellcome.org/coronavirus-covid-19/open-data
no DOI — not checkedTanMLeQVEfficientNet: Rethinking model scaling for convolutional neural networks201936th International Conference on Machine Learning, ICML 2019June 2019Long Beach, CA1069110700
no DOI — not checkedref30
no DOI — not checkedref33
no DOI — not checkedref64
no DOI — not checkedref65
no DOI — not checkedref73
no DOI — not checkedRahmanTCOVID-19 Radiography DatabaseKaggle2021-04-05https://www.kaggle.com/tawsifurrahman/covid19-radiography-database
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