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- Title
MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation.
- Authors
Zhang, Meifang; Sun, Qi; Cai, Fanggang; Yang, Changcai
- Abstract
The robust segmentation of organs from the medical image is the key technique in medical image analysis for disease diagnosis. U-Net is a robust structure for medical image segmentation. However, U-Net adopts consecutive downsampling encoders to capture multiscale features, resulting in the loss of contextual information and insufficient recovery of high-level semantic features. In this paper, we present a new multibranch hybrid attention network (MHA-Net) to capture more contextual information and high-level semantic features. The main idea of our proposed MHA-Net is to use the multibranch hybrid attention feature decoder to recover more high-level semantic features. The lightweight pyramid split attention (PSA) module is used to connect the encoder and decoder subnetwork to obtain a richer multiscale feature map. We compare the proposed MHA-Net to state-of-art approaches on the DRIVE dataset, the fluoroscopic roentgenographic stereophotogrammetric analysis X-ray dataset, and the polyp dataset. The experimental results on different modal images reveal that our proposed MHA-Net provides better segmentation results than other segmentation approaches.
- Subjects
DIAGNOSTIC imaging; IMAGE analysis; ARTIFICIAL neural networks; DIAGNOSIS; IMAGE segmentation; OPTICAL disk drives
- Publication
Computational & Mathematical Methods in Medicine, 2022, p1
- ISSN
1748-670X
- Publication type
Article
- DOI
10.1155/2022/8375981