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ID 118693
著者
Zhou, Yuxiang Tokushima University
任, 福継 University of Electronic Science and Technology of China 徳島大学 教育研究者総覧 KAKEN研究者をさがす
Lu, Huimin Kyushu Institute of Technology
Nakagawa, Satoshi The University of Tokyo
キーワード
U-Net
Dual attention
Attention gate
Depthwise separable convolution
Medical image segmentation
資料タイプ
学術雑誌論文
抄録
Automatic medical image segmentation method is highly needed to help experts in lesion segmentation. The deep learning technology emerging has profoundly driven the development of medical image segmentation. While U-Net and attention mechanisms are widely utilized in this field, the application of attention, albeit successful in natural scene image segmentation, tends to inflate the number of model parameters and neglects the potential for feature fusion between different convolutional layers. In response to these challenges, we present the Multi-Attention and Depthwise Separable Convolution U-Net (MDSU-Net), designed to enhance feature extraction. The multi-attention aspect of our framework integrates dual attention and attention gates, adeptly capturing rich contextual details and seamlessly fusing features across diverse convolutional layers. Additionally, our encoder integrates a depthwise separable convolution layer, streamlining the model’s complexity without sacrificing its efficacy, ensuring versatility across various segmentation tasks. The results demonstrate that our method outperforms state-of-the-art across three diverse medical image datasets.
掲載誌名
Neurocomputing
ISSN
09252312
18728286
cat書誌ID
AA10827402
AA11540468
出版者
Elsevier
564
開始ページ
126970
発行日
2023-10-29
備考
論文本文は2025-10-29以降公開予定
権利情報
© 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
EDB ID
出版社版DOI
出版社版URL
言語
eng
著者版フラグ
その他
部局
理工学系
病院