ISSN: 1899-0967
Polish Journal of Radiology
Established by prof. Zygmunt Grudziński in 1926 Sun
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1/2021
vol. 86
 
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Technology and contrast media
abstract:
Original paper

A fundamental study assessing the generalized fitting method in conjunction with every possible coalition of N-combinations (G-EPOC) using the appendicitis detection task of computed tomography

Tomoyuki Noguchi
1, 2, 3
,
Yumi Matsushita
1
,
Yusuke Kawata
4
,
Yoshitaka Shida
4
,
Akihiro Machitori
4

1.
Education and Training Office, Department of Clinical Research, Centre for Clinical Sciences, Japan
2.
Department of Radiology, National Hospital Organization Kyushu Medical Centre, Jigyohama, Chuo-ku, Fukuoka City, Fukuoka Province, Japan
3.
Department of Clinical Research, National Hospital Organization Kyushu Medical Centre, Jigyohama, Chuo-ku, Fukuoka City, Fukuoka Province, Japan
4.
Department of Radiology, National Centre for Global Health and Medicine, Japan
Pol J Radiol 2021; 86: e532-e541
Online publish date: 2021/09/13
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Introduction
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 computer-based diagnoses in medical imaging and could become an option for the selection of a reasonable K value in K-CV.

keywords:

neural networks (computer), machine learning, learning curve, computer simulation, appendicitis, cross validation




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