Automatic emphysema detection using weakly labeled HRCT lung images.
Isabel Pino PeñaVeronika CheplyginaSofia PaschaloudiMorten VuustJesper CarlUlla Møller WeinreichLasse Riis ØstergaardMarleen de BruijnePublished in: PloS one (2018)
The presented method uses MIL classifiers to automatically identify emphysema regions in HRCT scans. Furthermore, this approach has been demonstrated to correlate better with DLCO than a classical density based method or a radiologist, which is known to be affected in emphysema. Therefore, it is relevant to facilitate assessment of emphysema and to reduce inter-observer variability.
Keyphrases
- chronic obstructive pulmonary disease
- lung function
- pulmonary fibrosis
- idiopathic pulmonary fibrosis
- deep learning
- computed tomography
- cystic fibrosis
- convolutional neural network
- magnetic resonance imaging
- magnetic resonance
- pet imaging
- label free
- loop mediated isothermal amplification
- metal organic framework
- liquid chromatography
- neural network
- quantum dots
- sensitive detection
- pet ct