Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images

Abstract Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and the traditional variables extracted from computed tomography (CT) images may not be sufficient to describe all the topological features of lung tissues in COPD patients. We employed an unsupervised three-dimensional...

Description complète

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Frank Li, Jiwoong Choi, Chunrui Zou, John D. Newell, Alejandro P. Comellas, Chang Hyun Lee, Hongseok Ko, R. Graham Barr, Eugene R. Bleecker, Christopher B. Cooper, Fereidoun Abtin, Igor Barjaktarevic, David Couper, MeiLan Han, Nadia N. Hansel, Richard E. Kanner, Robert Paine, Ella A. Kazerooni, Fernando J. Martinez, Wanda O’Neal, Stephen I. Rennard, Benjamin M. Smith, Prescott G. Woodruff, Eric A. Hoffman, Ching-Long Lin
Format:	article
Langue:	EN
Publié:	Nature Portfolio 2021
Sujets:	Medicine R Science Q
Accès en ligne:	https://doaj.org/article/c5e067c40faa40fa966e6aa8bc55c3f2
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Soyez le premier à ajouter un commentaire!

Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images

Documents similaires