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Updated: Oct 12, 2025

Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
A fundamental study assessing the generalized fitting method in conjunction with every possible coalition of
Tomoyuki Noguchi1,2,3, Yumi Matsushita1, Yusuke Kawata4
1Education and Training Office, Department of Clinical Research, Centre for Clinical Sciences, Japan.
Purpose:
Increased use of deep learning (DL) in medical imaging diagnoses has led to more frequent use of 10-fold cross-validation (10-CV) for the evaluation of the performance of DL. To eliminate some of the (10-fold) repetitive processing in 10-CV, we proposed a "generalized fitting method in conjunction with every possible coalition of N-combinations (G-EPOC)", to estimate the range of the mean accuracy of 10-CV using less than 10 results of 10-CV.
Material And Methods:
G-EPOC was executed as follows. We first provided (2N-1) coalition subsets using a specified N, which was 9 or less, out of 10 result datasets of 10-CV. We then obtained the estimation range of the accuracy by applying those subsets to the distribution fitting twice using a combination of normal, binominal, or Poisson distributions. Using datasets of 10-CVs acquired from the practical detection task of the appendicitis on CT by DL, we scored the estimation success rates if the range provided by G-EPOC included the true accuracy.
Results:
G-EPOC successfully estimated the range of the mean accuracy by 10-CV at over 95% rates for datasets with N assigned as 2 to 9.
Conclusions:
G-EPOC will help lessen the consumption of time and computer resources in the development of computerbased diagnoses in medical imaging and could become an option for the selection of a reasonable K value in K-CV.
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