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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

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 conj...

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Autores principales: Noguchi, Tomoyuki, Matsushita, Yumi, Kawata, Yusuke, Shida, Yoshitaka, Machitori, Akihiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Termedia Publishing House 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8607834/
https://www.ncbi.nlm.nih.gov/pubmed/34820029
http://dx.doi.org/10.5114/pjr.2021.110309
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author Noguchi, Tomoyuki
Matsushita, Yumi
Kawata, Yusuke
Shida, Yoshitaka
Machitori, Akihiro
author_facet Noguchi, Tomoyuki
Matsushita, Yumi
Kawata, Yusuke
Shida, Yoshitaka
Machitori, Akihiro
author_sort Noguchi, Tomoyuki
collection PubMed
description 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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spelling pubmed-86078342021-11-23 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 Noguchi, Tomoyuki Matsushita, Yumi Kawata, Yusuke Shida, Yoshitaka Machitori, Akihiro Pol J Radiol Original Paper 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. Termedia Publishing House 2021-09-13 /pmc/articles/PMC8607834/ /pubmed/34820029 http://dx.doi.org/10.5114/pjr.2021.110309 Text en Copyright © Polish Medical Society of Radiology 2021 https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0). License (https://creativecommons.org/licenses/by-nc-nd/4.0/)
spellingShingle Original Paper
Noguchi, Tomoyuki
Matsushita, Yumi
Kawata, Yusuke
Shida, Yoshitaka
Machitori, Akihiro
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8607834/
https://www.ncbi.nlm.nih.gov/pubmed/34820029
http://dx.doi.org/10.5114/pjr.2021.110309
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