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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/18788
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| タイトル: | RDIU-Net: Lightweight Medical Image Segmentation Network |
| 著者: | Kurosawa, Juon
Elibol, Armagan
Chong, Nak Young |
| キーワード: | Medical Imaging
Image Segmentation
Deep Learning |
| 発行日: | 2023-10 |
| 出版者: | Institute of Control, Robotics and Systems (ICROS) |
| 誌名: | 2023 23rd International Conference on Control, Automation and Systems (ICCAS) |
| 開始ページ: | 964 |
| 終了ページ: | 968 |
| DOI: | 10.23919/ICCAS59377.2023.10316983 |
| 抄録: | In recent years, medical image segmentation using deep learning methods has become more and more popular and developed with the aim of both reducing human-related errors and the time required for manual segmentation. One of the pioneers in deep learning-based biological image segmentation networks, U-Net was proposed back in 2015. Since then, several models have been proposed to extend U-Net. However, the trade-off between computational complexity and accuracy remains a major challenge. To address this trade-off, we use a new Involution kernel for spatial information and propose a model lightweight medical image segmentation network, Residual Involution U-Net (RDIU-Net). Involution, Residual, and Dense structures are incorporated into the U-Net model to extract both channel and spatial features. Evaluations have been carried out on three different datasets of ultrasound, X-ray, and dermoscopic images. The proposed model RDIU-Net showed superior results in accuracy, processing speed, training stability, and convergence compared to U-Net. |
| Rights: | This is the author's version of the work. Copyright (C) ICROS. 2023 23rd International Conference on Control, Automation and Systems (ICCAS 2023), 2023, pp. 964-968. DOI: 10.23919/ICCAS59377.2023.10316983. Personal use of this material is permitted. This material is posted here with permission of Institute of Control, Robotics and Systems (ICROS). |
| URI: | http://hdl.handle.net/10119/18788 |
| 資料タイプ: | author |
| 出現コレクション: | b11-1. 会議発表論文・発表資料 (Conference Papers)
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このアイテムのファイル:
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| N-CHONG-I-1122-2.pdf | | 631Kb | Adobe PDF | 見る/開く |
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