| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-08-22 |
| タイトル |
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タイトル |
Information maximization-based clustering of histopathology images using deep learning |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Rumman, Mahfujul Islam
小野, 直亮
Ohuchida, Kenoki
Altaf-Ul-Amin, MD.
Huang, Ming
金谷, 重彦
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Pancreatic cancer is one of the most adverse diseases and it is very difficult to treat because the cancer cells formed in the pancreas intertwine themselves with nearby blood vessels and connective tissue. Hence, the surgical procedure of treatment becomes complicated and it does not always lead to a cure. Histopathological diagnosis is the usual approach for cancer diagnosis. However, the pancreas remains so deep inside the body that experts sometimes struggle to detect cancer in it. Computer-aided diagnosis can come to the aid of pathologists in this scenario. It assists experts by supporting their diagnostic decisions. In this research, we carried out a deep learning-based approach to analyze histopathology images. We collected whole-slide images of KPC mice to implement this work. The pancreatic abnormalities observed in KPC mice develop similar histological features to human beings. We created random patches from whole-slide images. Then, a convolutional autoencoder framework was used to embed these patches into an integrated latent space. We applied ‘information maximization’, a deep learning clustering technique to cluster the identical patches in an unsupervised manner since our dataset does not have annotation. Moreover, Uniform manifold approximation and projection, a nonlinear dimension reduction technique was utilized to visualize the embedded patches in a 2-dimensional space. Finally, we calculated a few internal cluster validation metrics to determine the optimal cluster set. Our work concentrated on patch-based anomaly detection in the whole slide histopathology images of KPC mice. |
| 書誌情報 |
en : PLOS Digital Health
巻 2,
号 12,
ページ数 23,
発行日 2023-12-08
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| 出版者 |
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出版者 |
Public Library of Science |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2767-3170 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1371/journal.pdig.0000391 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000391 |
| 権利 |
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権利情報Resource |
http://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
© 2023 Rumman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| 著者版フラグ |
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出版タイプ |
NA |
| 助成情報 |
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助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
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研究課題番号 |
21K12111 |
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研究課題番号URI |
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21K12111/ |
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研究課題名 |
分散学習ネットワークモデルを用いた病理組織画像の特徴抽出の最適化 |
