"The Reliability Issue of Computer-Aided Breast Cancer Diagnosis,"

Computers and Biomedical Research, Vol. 33, No. 4 (August 2000), pp. 296-313, 2000.

B. Kovalerchuk, E. Triantaphyllou, J.F. Ruiz, V.I. Torvik, and E. Vityaev

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 on 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.

Key Words:
Neural networks, machine learning, discriminant analysis, data monotonicity, computer-aided diagnostic systems, reliability of diagnosis, representation/narrow vicinity hypothesis.

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