CBAM-Unet++:easier to find the target with the attention module "CBAM"

There are already many methods based on U-net, however, due to the paricularity of medical images, we need to pay more attention to the target area to perform more detailed segmentation. In this paper, we present a CBAM-Unet++ module, which a more targeted architecture for medical image segmentation...

Full description

Saved in:
Bibliographic Details
Published in2021 IEEE 10th Global Conference on Consumer Electronics (GCCE) pp. 655 - 657
Main Authors Zhao, Zhengxuan, Chen, Kaixu, Yamane, Satoshi
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.10.2021
Subjects
Attention
Biomedical imaging
CBAM
Conferences
Consumer electronics
Image segmentation
Medical Image Segmentation
Unet
Online AccessGet full text

Cover

More Information
Summary:There are already many methods based on U-net, however, due to the paricularity of medical images, we need to pay more attention to the target area to perform more detailed segmentation. In this paper, we present a CBAM-Unet++ module, which a more targeted architecture for medical image segmentation. It combines Unet++ and Convolutional block attention module to make it easier for architecture to ignore irrelevant background information, thereby paying more attention to the parts that we want to have.
DOI:10.1109/GCCE53005.2021.9622008