Paper Info
Title
Automatic tissue characterization of air trapping in chest radiographs using deep neural networks
Abstract
Significant progress has been made in recent years for computer-aided diagnosis of abnormal pulmonary textures from computed tomography (CT) images. Similar initiatives in chest radiographs (CXR), the common modality for pulmonary diagnosis, are much less developed. CXR are fast, cost effective and low-radiation solution to diagnosis over CT. However, the subtlety of textures in CXR makes them hard to discern even by trained eye. We explore the performance of deep learning abnormal tissue characterization from CXR. Prior studies have used CT imaging to characterize air trapping in subjects with pulmonary disease; however, the use of CT in children is not recommended mainly due to concerns pertaining to radiation dosage. In this work, we present a stacked autoencoder (SAE) deep learning architecture for automated tissue characterization of air-trapping from CXR. To our best knowledge this is the first study applying deep learning framework for the specific problem on 51 CXRs, an F-score of ≈ 76.5% and a strong correlation with the expert visual scoring (R=0.93, p =<; 0.01) demonstrate the potential of the proposed method to characterization of air trapping.
Year
DOI
Venue
2016
10.1109/EMBC.2016.7590649
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Keywords
Field
DocType
Air,Diagnosis, Computer-Assisted,Humans,Image Processing, Computer-Assisted,Lung,Neural Networks (Computer),Radiography, Thoracic,Virus Diseases
Computer vision,Autoencoder,Computer science,Computed tomography,Radiography,Artificial intelligence,Radiology,Deep learning,Air trapping,Deep neural networks
Conference
Volume
ISSN
ISBN
2016
1557-170X
978-1-4577-0219-8
Citations 
PageRank 
References 
0
0.34
9
Authors
4
Name
Order
Citations
PageRank
Awais Mansoor16812.49
Geovanny F Perez231.43
Gustavo Nino311.02
Marius George Linguraru436248.94