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Proceedings Paper

Local-global classifier fusion for screening chest radiographs
Author(s): Meng Ding; Sameer Antani; Stefan Jaeger; Zhiyun Xue; Sema Candemir; Marc Kohli; George Thoma
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Paper Abstract

Tuberculosis (TB) is a severe comorbidity of HIV and chest x-ray (CXR) analysis is a necessary step in screening for the infective disease. Automatic analysis of digital CXR images for detecting pulmonary abnormalities is critical for population screening, especially in medical resource constrained developing regions. In this article, we describe steps that improve previously reported performance of NLM’s CXR screening algorithms and help advance the state of the art in the field. We propose a local-global classifier fusion method where two complementary classification systems are combined. The local classifier focuses on subtle and partial presentation of the disease leveraging information in radiology reports that roughly indicates locations of the abnormalities. In addition, the global classifier models the dominant spatial structure in the gestalt image using GIST descriptor for the semantic differentiation. Finally, the two complementary classifiers are combined using linear fusion, where the weight of each decision is calculated by the confidence probabilities from the two classifiers. We evaluated our method on three datasets in terms of the area under the Receiver Operating Characteristic (ROC) curve, sensitivity, specificity and accuracy. The evaluation demonstrates the superiority of our proposed local-global fusion method over any single classifier.

Paper Details

Date Published: 13 March 2017
PDF: 6 pages
Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 101380A (13 March 2017); doi: 10.1117/12.2252459
Show Author Affiliations
Meng Ding, National Library of Medicine (United States)
Sameer Antani, National Library of Medicine (United States)
Stefan Jaeger, National Library of Medicine (United States)
Zhiyun Xue, National Library of Medicine (United States)
Sema Candemir, National Library of Medicine (United States)
Marc Kohli, Univ. of California, San Francisco (United States)
George Thoma, National Library of Medicine (United States)

Published in SPIE Proceedings Vol. 10138:
Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications
Tessa S. Cook; Jianguo Zhang, Editor(s)

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