MA-Unet: an improved version of Unet based on multi-scale and attention mechanism for medical image segmentation

Yutong Cai; Yong Wang

doi:10.1117/12.2628519

7 March 2022 MA-Unet: an improved version of Unet based on multi-scale and attention mechanism for medical image segmentation

Yutong Cai, Yong Wang

Author Affiliations +

Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 121670X (2022) https://doi.org/10.1117/12.2628519
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China

Abstract

Convolutional neural network models have become one of the most commonly used methods for analyzing medical images. Among them, the codec structure has brought important breakthrough results for medical image segmentation. However, the current medical image segmentation method based on the codec network architecture still has many problems. The corresponding feature map of the codec network in the skip connection structure has a large semantic ambiguity, which may increase the difficulty of learning the network and reduce the segmentation performance. The codec network architecture cannot make full use of the relationship between objects in the global view, and also ignores the global context information of different scales. In this article, we add attention gate mechanism (AGs) to the jump connection structure, and introduce attention mechanism and multi-scale mechanism to solve the above problems. Our model obtains better segmentation performance while introducing fewer parameters.

Citation Download Citation

Yutong Cai and Yong Wang "MA-Unet: an improved version of Unet based on multi-scale and attention mechanism for medical image segmentation", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 121670X (7 March 2022); https://doi.org/10.1117/12.2628519

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