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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/18788

Title: RDIU-Net: Lightweight Medical Image Segmentation Network
Authors: Kurosawa, Juon
Elibol, Armagan
Chong, Nak Young
Keywords: Medical Imaging
Image Segmentation
Deep Learning
Issue Date: 2023-10
Publisher: Institute of Control, Robotics and Systems (ICROS)
Magazine name: 2023 23rd International Conference on Control, Automation and Systems (ICCAS)
Start page: 964
End page: 968
DOI: 10.23919/ICCAS59377.2023.10316983
Abstract: 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
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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