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Articles Published Processes
7/7/2020 10:45:31 AM | Browse: 695 | Download: 1294
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Received |
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2020-03-21 00:21 |
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Peer-Review Started |
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2020-03-21 00:21 |
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To Make the First Decision |
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Return for Revision |
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2020-04-22 00:00 |
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Revised |
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2020-05-02 02:05 |
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Second Decision |
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2020-06-05 08:13 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2020-06-07 03:51 |
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Articles in Press |
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2020-06-07 03:51 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2020-07-02 08:23 |
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Publish the Manuscript Online |
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2020-07-07 10:45 |
| ISSN |
2644-3228 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Pathology |
| Manuscript Type |
Basic Study |
| Article Title |
Impact of blurs on machine-learning aided digital pathology image analysis
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Maki Ogura, Tomoharu Kiyuna and Hiroshi Yoshida |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Hiroshi Yoshida, MD, PhD, Staff Physician, Department of Diagnostic Pathology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 1040045, Japan. hiroyosh@ncc.go.jp |
| Key Words |
Machine learning; Digital pathology image; Automated image analysis; Blur; Color; Reproducibility |
| Core Tip |
Little attention has been paid to the reproducibility of machine learning (ML)-based histological classification in heterochronously obtained Digital pathology images (DPIs) of the same hematoxylin and eosin slide. This study elucidated the frequency and preventable causes of discordant classification results of DPI analysis using ML for the heterochronously obtained DPIs. We observed discordant classification results in 23.1% of the paired DPIs obtained by two independent scans of the same microscope slide. The group with discordant classification results showed a significantly higher blur index than the other group. Our results suggest that differences in the blur of the paired DPIs may cause discordant classification results. |
| Publish Date |
2020-07-07 10:45 |
| Citation |
Ogura M, Kiyuna T, Yoshida H. Impact of blurs on machine-learning aided digital pathology image analysis. Artif Intell Cancer 2020; 1(1): 31-38 |
| URL |
https://www.wjgnet.com/2644-3228/full/v1/i1/31.htm |
| DOI |
https://dx.doi.org/10.35713/aic.v1.i1.31 |
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