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AMSUnet: A neural network using atrous multi-scale convolution for medical image segmentation.
Yin, Yunchou; Han, Zhimeng; Jian, Muwei; Wang, Gai-Ge; Chen, Liyan; Wang, Rui.
Afiliación
  • Yin Y; School of Computer Science and Technology, Ocean University of China, Qingdao, China.
  • Han Z; School of Computer Science and Technology, Ocean University of China, Qingdao, China.
  • Jian M; School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China; School of Information Science and Technology, Linyi University, Linyi, China. Electronic address: 20173016@sdufe.edu.cn.
  • Wang GG; School of Computer Science and Technology, Ocean University of China, Qingdao, China. Electronic address: wgg@ouc.edu.cn.
  • Chen L; Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, China.
  • Wang R; College of Systems Engineering, National University of Defense Technology, Changsha, China; Xiangjiang Laboratory, Changsha, China.
Comput Biol Med ; 162: 107120, 2023 08.
Article en En | MEDLINE | ID: mdl-37276753
In recent years, Unet and its variants have gained astounding success in the realm of medical image processing. However, some Unet variant networks enhance their performance while increasing the number of parameters tremendously. For lightweight and performance enhancement jointly considerations, inspired by SegNeXt, we develop a medical image segmentation network model using atrous multi-scale (AMS) convolution, named AMSUnet. In particular, we construct a convolutional attention block AMS using atrous and multi-scale convolution, and redesign the downsampling encoder based on this block, called AMSE. To enhance feature fusion, we design a residual attention mechanism module (i.e., RSC) and apply it to the skip connection. Compared with existing models, our model only needs 2.62 M parameters to achieve the purpose of lightweight. According to experimental results on various datasets, the segmentation performance of the designed model is superior for small, medium, and large-scale targets. Code will be available at https://github.com/llluochen/AMSUnet.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Redes Neurales de la Computación Idioma: En Revista: Comput Biol Med Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
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Imprimir
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PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Redes Neurales de la Computación Idioma: En Revista: Comput Biol Med Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos