COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images.
暂无分享,去创建一个
Wei Li | Yingjian Yang | Yingwei Guo | Na Zeng | Shicong Wang | Ziran Chen | Yang Liu | Xiaoqiang Miao | Rongchang Chen | Yan Kang | Xingguang Deng | Jiaxuan Xu | Haseeb Hassan
[1] Rongchang Chen,et al. Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD , 2022, Frontiers in Medicine.
[2] Jose M. Sanchez-Bornot,et al. Alzheimer's disease classification using cluster‐based labelling for graph neural network on heterogeneous data , 2022, Healthcare technology letters.
[3] Rongchang Chen,et al. Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier. , 2022, Mathematical biosciences and engineering : MBE.
[4] Rongchang Chen,et al. Early COPD Risk Decision for Adults Aged From 40 to 79 Years Based on Lung Radiomics Features , 2022, Frontiers in Medicine.
[5] E. Hoffman,et al. Quantitative Chest CT Assessment of Small Airways Disease in Post-Acute SARS-CoV-2 Infection , 2022, Radiology.
[6] Rongchang Chen,et al. A novel lung radiomics feature for characterizing resting heart rate and COPD stage evolution based on radiomics feature combination strategy. , 2022, Mathematical biosciences and engineering : MBE.
[7] Kewu Huang,et al. A Novel CT-Based Radiomics Features Analysis for Identification and Severity Staging of COPD. , 2022, Academic radiology.
[8] Jinrong Yang,et al. Total Lung and Lobar Quantitative Assessment Based on Paired Inspiratory–Expiratory Chest CT in Healthy Adults: Correlation with Pulmonary Ventilatory Function , 2021, Diagnostics.
[9] Jae Seung Lee,et al. Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease , 2021, Scientific Reports.
[10] Jae Seung Lee,et al. Radiomics approach for survival prediction in chronic obstructive pulmonary disease , 2021, European Radiology.
[11] Sanghun Choi,et al. A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects , 2021, Scientific Reports.
[12] S. Röhrich,et al. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem , 2020, European Radiology Experimental.
[13] H. Kauczor,et al. Quantitative CT detects progression in COPD patients with severe emphysema in a 3-month interval , 2020, European Radiology.
[14] Xiaowei Ding,et al. Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation , 2019, Medical Image Anal..
[15] Meilan K. Han,et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease: the GOLD science committee report 2019 , 2019, European Respiratory Journal.
[16] Zhiyuan Liu,et al. Graph Neural Networks: A Review of Methods and Applications , 2018, AI Open.
[17] Syed Muhammad Anwar,et al. Medical Image Analysis using Convolutional Neural Networks: A Review , 2017, Journal of Medical Systems.
[18] I. Gut,et al. Emphysema- and airway-dominant COPD phenotypes defined by standardised quantitative computed tomography , 2016, European Respiratory Journal.
[19] Edwin K Silverman,et al. CT-Definable Subtypes of Chronic Obstructive Pulmonary Disease: A Statement of the Fleischner Society. , 2015, Radiology.
[20] D. Lynch. Progress in Imaging COPD, 2004 - 2014. , 2014, Chronic obstructive pulmonary diseases.
[21] Shiyuan Liu,et al. Characteristic features of pulmonary function test, CT volume analysis and MR perfusion imaging in COPD patients with different HRCT phenotypes , 2014, The clinical respiratory journal.
[22] C. Dine,et al. Lung volumes: measurement, clinical use, and coding. , 2012, Chest.
[23] Ella A. Kazerooni,et al. CT-based Biomarker Provides Unique Signature for Diagnosis of COPD Phenotypes and Disease Progression , 2012, Nature Medicine.
[24] Patrick Granton,et al. Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.
[25] T. Honda,et al. Clinical analysis of chronic obstructive pulmonary disease phenotypes classified using high‐resolution computed tomography , 2006, Respirology.
[26] S. Hurd,et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease: GOLD Executive Summary Updated 2003 , 2004, COPD.