The reliability issue of computer-aided breast cancer diagnosis

Boris Kovalerchuk; Evangelos Triantaphyllou; James F. Ruiz; Vetle I. Torvik; Evgeni Vityaev

doi:10.1006/cbmr.2000.1546

The reliability issue of computer-aided breast cancer diagnosis

Boris Kovalerchuk, Evangelos Triantaphyllou, James F. Ruiz, Vetle I. Torvik, Evgeni Vityaev

Research output: Contribution to journal › Article › peer-review

Abstract

This paper introduces a number of reliability criteria for computer-aided diagnostic systems for breast cancer. These criteria are then used to analyze some published neural network systems. It is also shown that the property of monotonicity for the data is rather natural in this medical domain, and it has the potential to significantly improve the reliability of breast cancer diagnosis while maintaining a general representation power. A central part of this paper is devoted to the representation/narrow vicinity hypothesis, upon which existing computer-aided diagnostic methods heavily rely. The paper also develops a framework for determining the validity of this hypothesis. The same framework can be used to construct a diagnostic procedure with improved reliability. (C) 2000 Academic Press.

Original language	English (US)
Pages (from-to)	296-313
Number of pages	18
Journal	Computers and Biomedical Research
Volume	33
Issue number	4
DOIs	https://doi.org/10.1006/cbmr.2000.1546
State	Published - 2000
Externally published	Yes

Keywords

Computer-aided diagnostic systems
Data monotonicity
Discriminant analysis
Machine learning
Neural networks
Reliability of diagnosis
Representation/narrow vicinity hypothesis

ASJC Scopus subject areas

Medicine (miscellaneous)

Online availability

10.1006/cbmr.2000.1546

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title = "The reliability issue of computer-aided breast cancer diagnosis",

abstract = "This paper introduces a number of reliability criteria for computer-aided diagnostic systems for breast cancer. These criteria are then used to analyze some published neural network systems. It is also shown that the property of monotonicity for the data is rather natural in this medical domain, and it has the potential to significantly improve the reliability of breast cancer diagnosis while maintaining a general representation power. A central part of this paper is devoted to the representation/narrow vicinity hypothesis, upon which existing computer-aided diagnostic methods heavily rely. The paper also develops a framework for determining the validity of this hypothesis. The same framework can be used to construct a diagnostic procedure with improved reliability. (C) 2000 Academic Press.",

keywords = "Computer-aided diagnostic systems, Data monotonicity, Discriminant analysis, Machine learning, Neural networks, Reliability of diagnosis, Representation/narrow vicinity hypothesis",

author = "Boris Kovalerchuk and Evangelos Triantaphyllou and Ruiz, {James F.} and Torvik, {Vetle I.} and Evgeni Vityaev",

note = "Funding Information: 1The first two authors gratefully recognize the partial support from the Office of Naval Research (ONR) Grant N00014-95-1-0639. The first, second, and fifth authors also recognize the partial support from the CAST and COBASE programs at the National Research Council (NRC). The second and fourth authors recognize the partial support from ONR Grant N0001-97-1-0632. 2To whom correspondence should be addressed. Web: http://www.imse.lsu.edu/vangelis.",

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doi = "10.1006/cbmr.2000.1546",

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AU - Triantaphyllou, Evangelos

AU - Ruiz, James F.

AU - Torvik, Vetle I.

AU - Vityaev, Evgeni

N1 - Funding Information: 1The first two authors gratefully recognize the partial support from the Office of Naval Research (ONR) Grant N00014-95-1-0639. The first, second, and fifth authors also recognize the partial support from the CAST and COBASE programs at the National Research Council (NRC). The second and fourth authors recognize the partial support from ONR Grant N0001-97-1-0632. 2To whom correspondence should be addressed. Web: http://www.imse.lsu.edu/vangelis.

PY - 2000

Y1 - 2000

N2 - This paper introduces a number of reliability criteria for computer-aided diagnostic systems for breast cancer. These criteria are then used to analyze some published neural network systems. It is also shown that the property of monotonicity for the data is rather natural in this medical domain, and it has the potential to significantly improve the reliability of breast cancer diagnosis while maintaining a general representation power. A central part of this paper is devoted to the representation/narrow vicinity hypothesis, upon which existing computer-aided diagnostic methods heavily rely. The paper also develops a framework for determining the validity of this hypothesis. The same framework can be used to construct a diagnostic procedure with improved reliability. (C) 2000 Academic Press.

AB - This paper introduces a number of reliability criteria for computer-aided diagnostic systems for breast cancer. These criteria are then used to analyze some published neural network systems. It is also shown that the property of monotonicity for the data is rather natural in this medical domain, and it has the potential to significantly improve the reliability of breast cancer diagnosis while maintaining a general representation power. A central part of this paper is devoted to the representation/narrow vicinity hypothesis, upon which existing computer-aided diagnostic methods heavily rely. The paper also develops a framework for determining the validity of this hypothesis. The same framework can be used to construct a diagnostic procedure with improved reliability. (C) 2000 Academic Press.

KW - Computer-aided diagnostic systems

KW - Data monotonicity

KW - Discriminant analysis

KW - Machine learning

KW - Neural networks

KW - Reliability of diagnosis

KW - Representation/narrow vicinity hypothesis

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The reliability issue of computer-aided breast cancer diagnosis

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Keywords

ASJC Scopus subject areas

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