MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation.

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 rich

SUBMITTER: Zhang M 

PROVIDER: S-EPMC9560845 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature