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Lung tissue characterization for emphysema differential diagnosis using deep convolutional neural networks
Author(s): Mohammadreza Negahdar; David Beymer
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Paper Abstract

In this study, we propose and validate an end-to-end pipeline based on deep learning for differential diagnosis of emphysema in thoracic CT images. The five lung tissue patterns involved in most differential restrictive and obstructive lung disease diagnoses include: emphysema, ground glass, fibrosis, micronodule, and normal. Four established network architectures have been trained and evaluated. To the best of our knowledge, this is the first comprehensive end-to-end deep CNN pipeline for differential diagnosis of emphysema. A comparative analysis shows the performance of the proposed models on two publicly available datasets.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109503R (13 March 2019); doi: 10.1117/12.2513044
Show Author Affiliations
Mohammadreza Negahdar, IBM Research - Almaden (United States)
David Beymer, IBM Research - Almaden (United States)

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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