 |
English
|
正體中文
|
简体中文
|
Post-Print筆數 : 27 |
全文筆數/總筆數 : 118658/149719 (79%)
造訪人次 : 80221632
線上人數 : 158
|
|
|
資料載入中.....
|
請使用永久網址來引用或連結此文件:
https://nccur.lib.nccu.edu.tw/handle/140.119/150955
|
| 題名: | Interactive content-based image retrieval with deep learning for CT abdominal organ recognition |
| 作者: | 羅崇銘
Lo, Chung-Ming;Wang, Chi-Cheng;Hung, Peng-Hsiang |
| 貢獻者: | 圖檔所 |
| 關鍵詞: | abdominal CT;content-based image retrieval;deep learning;vision transformer |
| 日期: | 2024-02 |
| 上傳時間: | 2024-04-29 13:19:20 (UTC+8) |
| 摘要: | Objective. Recognizing the most relevant seven organs in an abdominal computed tomography (CT) slice requires sophisticated knowledge. This study proposed automatically extracting relevant features and applying them in a content-based image retrieval (CBIR) system to provide similar evidence for clinical use. Approach. A total of 2827 abdominal CT slices, including 638 liver, 450 stomach, 229 pancreas, 442 spleen, 362 right kidney, 424 left kidney and 282 gallbladder tissues, were collected to evaluate the proposed CBIR in the present study. Upon fine-tuning, high-level features used to automatically interpret the differences among the seven organs were extracted via deep learning architectures, including DenseNet, Vision Transformer (ViT), and Swin Transformer v2 (SwinViT). Three images with different annotations were employed in the classification and query. Main results. The resulting performances included the classification accuracy (94%–99%) and retrieval result (0.98–0.99). Considering global features and multiple resolutions, SwinViT performed better than ViT. ViT also benefited from a better receptive field to outperform DenseNet. Additionally, the use of hole images can obtain almost perfect results regardless of which deep learning architectures are used. Significance. The experiment showed that using pretrained deep learning architectures and fine-tuning with enough data can achieve successful recognition of seven abdominal organs. The CBIR system can provide more convincing evidence for recognizing abdominal organs via similarity measurements, which could lead to additional possibilities in clinical practice. |
| 關聯: | Physics in Medicine & Biology, Vol.69, No.4, 045004 |
| 資料類型: | article |
| DOI 連結: | https://doi.org/10.1088/1361-6560/ad1f86 |
| DOI: | 10.1088/1361-6560/ad1f86 |
| 顯示於類別: | [圖書資訊與檔案學研究所] 期刊論文
|
文件中的檔案:
| 檔案 |
描述 |
大小 | 格式 | 瀏覽次數 |
|---|
| index.html | | 0Kb | HTML | 396 | 檢視/開啟 |
|
在政大典藏中所有的資料項目都受到原著作權保護.
|
著作權政策宣告 Copyright Announcement1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.
2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(
nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(
nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
