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A trial deep learning-based model for four-class histologic classification of colonic tumor from narrow band imaging
http://hdl.handle.net/10129/0002001882
http://hdl.handle.net/10129/0002001882760a477c-42b8-4693-8cfc-66fa7825c114
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本文 (1.5 MB)
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審査要旨 (275 KB)
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| アイテムタイプ | リポジトリ登録用アイテムタイプ（シンプル）(1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2025-11-27 | |||||||||
| タイトル | ||||||||||
| タイトル | A trial deep learning-based model for four-class histologic classification of colonic tumor from narrow band imaging | |||||||||
| 言語 | en | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| その他のタイトル | ||||||||||
| その他のタイトル | 深層学習モデルによる大腸腫瘍の狭帯域画像４クラス組織学的分類に関する試験的研究 | |||||||||
| 言語 | ja | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Computational biology and bioinformatics | |||||||||
| キーワード | ||||||||||
| 言語 | en | |||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Computational models | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_db06 | |||||||||
| 資源タイプ | doctoral thesis | |||||||||
| アクセス権 | ||||||||||
| アクセス権 | open access | |||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||
| 著者 |
清水, 孟
× 清水, 孟
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | Narrow band imaging (NBI) has been extensively utilized as a diagnostic tool for colorectal neoplastic lesions. This study aimed to develop a trial deep learning (DL) based four-class classification model for low-grade dysplasia (LGD); high-grade dysplasia or mucosal carcinoma (HGD); superficially invasive submucosal carcinoma (SMs) and deeply invasive submucosal carcinomas (SMd) and evaluate its potential as a diagnostic tool. We collected a total of 1,390 NBI images as the dataset, including 53 LGD, 120 HGD, 20 SMs and 17 SMd. A total of 598,801 patches were trimmed from the lesion and background. A patch-based classification model was built by employing a residual convolutional neural network (CNN) and validated by three-fold cross-validation. The patch-based validation accuracy was 0.876, 0.957, 0.907 and 0.929 in LGD, HGD, SMs and SMd, respectively. The image-level classification algorithm was derived from the patch-based mapping across the entire image domain, attaining accuracies of 0.983, 0.990, 0.964, and 0.992 in LGD, HGD, SMs, and SMd, respectively. Our CNN-based model demonstrated high performance for categorizing the histological grade of dysplasia as well as the depth of invasion in routine colonoscopy, suggesting a potential diagnostic tool with minimal human inputs. | |||||||||
| 言語 | en | |||||||||
| 内容記述 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Author(s): Shimizu, Takeshi ; Sasaki, Yoshihiro ; Ito, Kei ; Matsuzaka, Masashi ; Sakuraba, Hirotake ; Fukuda, Shinsaku | |||||||||
| 言語 | en | |||||||||
| 書誌情報 |
en : Scientific Reports 巻 13, p. 7510, 発行日 2023-05-09 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | EISSN | |||||||||
| 収録物識別子 | 2045-2322 | |||||||||
| DOI | ||||||||||
| 関連タイプ | isIdenticalTo | |||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | https://doi.org/10.1038/s41598-023-34750-3 | |||||||||
| 権利情報 | ||||||||||
| 権利情報 | © 2023, The Author(s) | |||||||||
| 言語 | en | |||||||||
| 権利情報 | ||||||||||
| 権利情報 | This is an open access article distributed under the terms of the Creative Commons CC BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | |||||||||
| 言語 | en | |||||||||
| 出版タイプ | ||||||||||
| 出版タイプ | VoR | |||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||
| 出版者 | ||||||||||
| 出版者 | Springer Nature | |||||||||
| 言語 | en | |||||||||
| 学位名 | ||||||||||
| 学位名 | 博士（医学） | |||||||||
| 言語 | ja | |||||||||
| 学位授与機関 | ||||||||||
| 学位授与機関名 | 弘前大学 | |||||||||
| 言語 | ja | |||||||||
| 学位授与年月日 | ||||||||||
| 学位授与年月日 | 2025-09-30 | |||||||||
| 学位授与番号 | ||||||||||
| 学位授与番号 | 乙第896号 | |||||||||
