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Title: An adaptive incremental approach to constructing ensemble classifiers: Application in an information-theoretic computer-aided decision system for detection of masses in mammograms

Abstract

Ensemble classifiers have been shown efficient in multiple applications. In this article, the authors explore the effectiveness of ensemble classifiers in a case-based computer-aided diagnosis system for detection of masses in mammograms. They evaluate two general ways of constructing subclassifiers by resampling of the available development dataset: Random division and random selection. Furthermore, they discuss the problem of selecting the ensemble size and propose two adaptive incremental techniques that automatically select the size for the problem at hand. All the techniques are evaluated with respect to a previously proposed information-theoretic CAD system (IT-CAD). The experimental results show that the examined ensemble techniques provide a statistically significant improvement (AUC=0.905{+-}0.024) in performance as compared to the original IT-CAD system (AUC=0.865{+-}0.029). Some of the techniques allow for a notable reduction in the total number of examples stored in the case base (to 1.3% of the original size), which, in turn, results in lower storage requirements and a shorter response time of the system. Among the methods examined in this article, the two proposed adaptive techniques are by far the most effective for this purpose. Furthermore, the authors provide some discussion and guidance for choosing the ensemble parameters.

Authors:
; ;  [1]
  1. Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, Durham, North Carolina 27705 (United States)
Publication Date:
OSTI Identifier:
22100554
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 36; Journal Issue: 7; Other Information: (c) 2009 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; ALGORITHMS; BIOMEDICAL RADIOGRAPHY; DIAGNOSIS; IMAGE PROCESSING; MAMMARY GLANDS

Citation Formats

Mazurowski, Maciej A., Zurada, Jacek M., Tourassi, Georgia D., Department of Electrical and Computer Engineering, Computational Intelligence Laboratory, University of Louisville, Louisville, Kentucky 40292, and Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, Durham, North Carolina 27705. An adaptive incremental approach to constructing ensemble classifiers: Application in an information-theoretic computer-aided decision system for detection of masses in mammograms. United States: N. p., 2009. Web. doi:10.1118/1.3132304.
Mazurowski, Maciej A., Zurada, Jacek M., Tourassi, Georgia D., Department of Electrical and Computer Engineering, Computational Intelligence Laboratory, University of Louisville, Louisville, Kentucky 40292, & Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, Durham, North Carolina 27705. An adaptive incremental approach to constructing ensemble classifiers: Application in an information-theoretic computer-aided decision system for detection of masses in mammograms. United States. https://doi.org/10.1118/1.3132304
Mazurowski, Maciej A., Zurada, Jacek M., Tourassi, Georgia D., Department of Electrical and Computer Engineering, Computational Intelligence Laboratory, University of Louisville, Louisville, Kentucky 40292, and Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, Durham, North Carolina 27705. 2009. "An adaptive incremental approach to constructing ensemble classifiers: Application in an information-theoretic computer-aided decision system for detection of masses in mammograms". United States. https://doi.org/10.1118/1.3132304.
@article{osti_22100554,
title = {An adaptive incremental approach to constructing ensemble classifiers: Application in an information-theoretic computer-aided decision system for detection of masses in mammograms},
author = {Mazurowski, Maciej A. and Zurada, Jacek M. and Tourassi, Georgia D. and Department of Electrical and Computer Engineering, Computational Intelligence Laboratory, University of Louisville, Louisville, Kentucky 40292 and Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, Durham, North Carolina 27705},
abstractNote = {Ensemble classifiers have been shown efficient in multiple applications. In this article, the authors explore the effectiveness of ensemble classifiers in a case-based computer-aided diagnosis system for detection of masses in mammograms. They evaluate two general ways of constructing subclassifiers by resampling of the available development dataset: Random division and random selection. Furthermore, they discuss the problem of selecting the ensemble size and propose two adaptive incremental techniques that automatically select the size for the problem at hand. All the techniques are evaluated with respect to a previously proposed information-theoretic CAD system (IT-CAD). The experimental results show that the examined ensemble techniques provide a statistically significant improvement (AUC=0.905{+-}0.024) in performance as compared to the original IT-CAD system (AUC=0.865{+-}0.029). Some of the techniques allow for a notable reduction in the total number of examples stored in the case base (to 1.3% of the original size), which, in turn, results in lower storage requirements and a shorter response time of the system. Among the methods examined in this article, the two proposed adaptive techniques are by far the most effective for this purpose. Furthermore, the authors provide some discussion and guidance for choosing the ensemble parameters.},
doi = {10.1118/1.3132304},
url = {https://www.osti.gov/biblio/22100554}, journal = {Medical Physics},
issn = {0094-2405},
number = 7,
volume = 36,
place = {United States},
year = {Wed Jul 15 00:00:00 EDT 2009},
month = {Wed Jul 15 00:00:00 EDT 2009}
}