| Item type |
文献 / Documents(1) |
| 公開日 |
2023-11-28 |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 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/10.1016/j.neucom.2023.126970 |
|
|
関連名称 |
10.1016/j.neucom.2023.126970 |
| 出版タイプ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| タイトル |
|
|
タイトル |
A multi-attention and depthwise separable convolution network for medical image segmentation |
| 著者 |
Zhou, Yuxiang
康, 鑫
任, 福継
Lu, Huimin
ナカガワ, サトシ
単, 暁
|
| 抄録 |
|
|
内容記述 |
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. |
| キーワード |
|
|
主題 |
U-Net |
| キーワード |
|
|
主題 |
Dual attention |
| キーワード |
|
|
主題 |
Attention gate |
| キーワード |
|
|
主題 |
Depthwise separable convolution |
| キーワード |
|
|
主題 |
Medical image segmentation |
| 書誌情報 |
en : Neurocomputing
巻 564,
p. 126970,
発行日 2023-10-29
|
| 収録物ID |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
09252312 |
| 収録物ID |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
18728286 |
| 収録物ID |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA10827402 |
| 収録物ID |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA11540468 |
| 出版者 |
|
|
出版者 |
Elsevier |
| 権利情報 |
|
|
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/ |
| EID |
|
|
識別子 |
403604 |
| 言語 |
|
|
言語 |
eng |
neucom_564_126970.pdf (1.55 MB)
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