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Looking Closer to the Transferability Between Natural and Medical Images in Deep Learning
https://tokushima-u.repo.nii.ac.jp/records/2011943
https://tokushima-u.repo.nii.ac.jp/records/20119431bcf560b-099f-4647-be9e-783ee10ae00b
| 名前 / ファイル | ライセンス | アクション |
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access_11_79838.pdf (1.31 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||||||||||||
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| 公開日 | 2024-06-14 | |||||||||||||||||||||||
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| アクセス権 | open access | |||||||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||||||
| 出版社版DOI | ||||||||||||||||||||||||
| 関連識別子 | https://doi.org/10.1109/ACCESS.2023.3299819 | |||||||||||||||||||||||
| 関連名称 | 10.1109/ACCESS.2023.3299819 | |||||||||||||||||||||||
| 出版タイプ | ||||||||||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||||||||
| タイトル | ||||||||||||||||||||||||
| タイトル | Looking Closer to the Transferability Between Natural and Medical Images in Deep Learning | |||||||||||||||||||||||
| タイトル別表記 | ||||||||||||||||||||||||
| その他のタイトル | Looking Closer to the Transferability Between Natural and Medical Images | |||||||||||||||||||||||
| 著者 |
Rufaida, Syahidah Izza
× Rufaida, Syahidah Izza
× Putra, Tryan Aditya
× Leu, Jenq-Shiou
× 宋, 天
WEKO
1231
× 片山, 貴文
WEKO
976
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| 抄録 | ||||||||||||||||||||||||
| 内容記述 | Transfer-learning has rapidly become one of the most sophisticated and effective techniques in dealing with medical datasets. The most common transfer-learning method uses of a state-of-the-art model and its corresponding parameters as the starting point for new tasks. Recent studies have found that transfer-learning between medical and natural images has minimal advantages, attributed to their different characteristics, even with sufficient data and iterations. This study employs a meta-learning technique, building upon the traditional transfer learning approach, to explore the potential of natural tasks as a starting point for analyzing medical images. In addition, this study investigates the performance of transferring the searched augmentation from natural to medical images. Several studies proposing search algorithms for data augmentation argue that the augmentation techniques can be effectively transferred across different datasets. The results revealed that the transferability between natural and medical images leads to reduced performance owing to the characteristic difference between medical and natural searched augmentation. | |||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||
| 主題 | Data augmentation | |||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||
| 主題 | medical image dataset | |||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||
| 主題 | meta-learning | |||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||
| 主題 | natural images dataset | |||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||
| 主題 | transfer-learning | |||||||||||||||||||||||
| 書誌情報 |
en : IEEE Access 巻 11, p. 79838-79850, 発行日 2023-07-28 |
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| 収録物識別子タイプ | ISSN | |||||||||||||||||||||||
| 収録物識別子 | 21693536 | |||||||||||||||||||||||
| 出版者 | ||||||||||||||||||||||||
| 出版者 | IEEE | |||||||||||||||||||||||
| 権利情報 | ||||||||||||||||||||||||
| 権利情報 | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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| 識別子 | 399068 | |||||||||||||||||||||||
| 言語 | ||||||||||||||||||||||||
| 言語 | eng | |||||||||||||||||||||||
