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Unsupervised Clustering Of Airway Tree Structures On High-Resolution CT: The Mesa Lung Study., , , , , , , and . ISBI, page 1568-1572. IEEE, (2021)Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning., , , , , , , and . CoRR, (2020)Observational Study of Hydroxychloroquine in Hospitalized Patients with Covid-19, , , , , , , , , and 2 other author(s). New England Journal of Medicine, 0 (0): null (2020)Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans., , , , , , , , , and 2 other author(s). CoRR, (2021)Enhanced Generative Model for Unsupervised Discovery of Spatially-Informed Macroscopic Emphysema: The Mesa Copd Study., , , , , , , , and . ISBI, page 1212-1215. IEEE, (2019)Unsupervised Domain Adaption With Adversarial Learning (UDAA) for Emphysema Subtyping on Cardiac CT Scans: The Mesa Study., , , , , and . ISBI, page 289-293. IEEE, (2019)Emphysema Quantification on Cardiac CT Scans Using Hidden Markov Measure Field Model: The MESA Lung Study., , , , , , , and . MICCAI (2), volume 9901 of Lecture Notes in Computer Science, page 624-631. (2016)Unsupervised Discovery of Spatially-Informed Lung Texture Patterns for Pulmonary Emphysema: The MESA COPD Study., , , , , , , , and . MICCAI (1), volume 10433 of Lecture Notes in Computer Science, page 116-124. Springer, (2017)Emphysema quantification from CT scans using novel application of diaphragm curvature estimation: comparison with standard quantification methods and pulmonary function data., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 7260 of SPIE Proceedings, page 726032. SPIE, (2009)Adaptive Quantification and Longitudinal Analysis of Pulmonary Emphysema With a Hidden Markov Measure Field Model., , , , and . IEEE Trans. Med. Imaging, 33 (7): 1527-1540 (2014)