Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks
暂无分享,去创建一个
Piotr J. Slomka | Damini Dey | Gianfranco Parati | Pal Maurovich-Horvat | Andrew Lin | Gianluca Pontone | Cato Chan | Barry D. Pressman | Franco Cernigliaro | Aditya Killekar | Camilla Torlasco | Kajetan Grodecki | Sebastien Cadet | Priscilla McElhinney | Aryabod Razipour | Peter Julien | Judit Simon | Nicola Gaibazzi | Udit Thakur | Elisabetta Mancini | Cecilia Agalbato | Jiro Munechika | Hidenari Matsumoto | Roberto Menè | Nitesh Nerlekar | D. Dey | G. Pontone | P. Slomka | G. Parati | P. Maurovich-Horvat | S. Cadet | N. Gaibazzi | Franco Cernigliaro | K. Grodecki | A. Lin | A. Razipour | P. McElhinney | Cato Chan | B. Pressman | P. Julien | U. Thakur | E. Mancini | C. Agalbato | R. Menè | N. Nerlekar | C. Torlasco | J. Simon | J. Munechika | Hidenari Matsumoto | A. Killekar | R. Mené
[1] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[2] C. Zheng,et al. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19) , 2020 .
[3] Zhiyong Xu,et al. A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images , 2020, IEEE Transactions on Medical Imaging.
[4] Dit-Yan Yeung,et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.
[5] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] S. P. Morozov,et al. MosMedData: Chest CT Scans with COVID-19 Related Findings , 2020, medRxiv.
[7] Yang Wang,et al. Region Mutual Information Loss for Semantic Segmentation , 2019, NeurIPS.
[8] C. Catalano,et al. Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis , 2020, European Radiology.
[9] Seyed-Ahmad Ahmadi,et al. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).
[10] Ran Yang,et al. Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19 , 2020, Radiology. Cardiothoracic imaging.
[11] Tianqi Chen,et al. Empirical Evaluation of Rectified Activations in Convolutional Network , 2015, ArXiv.
[12] W. Liang,et al. Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography , 2020, Cell.
[13] Raymond Y Huang,et al. Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT , 2021, Radiology.
[14] M. Saatchi,et al. A meta-analysis of accuracy and sensitivity of chest CT and RT-PCR in COVID-19 diagnosis , 2020, Scientific Reports.
[15] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[16] Q. Tao,et al. Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases , 2020, Radiology.
[17] Adnan Saood. COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet , 2021, BMC Medical Imaging.
[18] Piotr J. Slomka,et al. Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT , 2018, IEEE Transactions on Medical Imaging.
[19] Z. Fayad,et al. Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection , 2020, Radiology.
[20] D. Dey,et al. Quantitative Burden of COVID-19 Pneumonia on Chest CT Predicts Adverse Outcomes: A Post-Hoc Analysis of a Prospective International Registry , 2020, Radiology. Cardiothoracic imaging.
[21] R. Pontarolo,et al. Systematic review with meta-analysis of the accuracy of diagnostic tests for COVID-19 , 2020, American Journal of Infection Control.
[22] D. Dey,et al. Epicardial adipose tissue is associated with extent of pneumonia and adverse outcomes in patients with COVID-19 , 2020, Metabolism.
[23] Prognostic Value and Reproducibility of AI-assisted Analysis of Lung Involvement in COVID-19 on Low-Dose Submillisievert Chest CT: Sample Size Implications for Clinical Trials , 2020, Radiology. Cardiothoracic imaging.
[24] Karan Sapra,et al. Hierarchical Multi-Scale Attention for Semantic Segmentation , 2020, ArXiv.
[25] K. Cao,et al. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy , 2020 .
[26] Heshui Shi,et al. Temporal Changes of CT Findings in 90 Patients with COVID-19 Pneumonia: A Longitudinal Study , 2020, Radiology.
[27] Dorin Comaniciu,et al. Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT , 2020, Radiology. Artificial intelligence.
[28] Kunwei Li,et al. CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19) , 2020, European Radiology.
[29] Thomas Brox,et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.