| Paper ID | BIO-8.2 |
| Paper Title |
CHANNEL ATTENTION RESIDUAL U-NET FOR RETINAL VESSEL SEGMENTATION |
| Authors |
Changlu Guo, Márton Szemenyei, Budapest University of Technology and Economics, Hungary; Yangtao Hu, Hospital of the Peoples Liberation Army Joint Logistics Support Force, China; Wenle Wang, Jiangxi Normal University, China; Wei Zhou, Shenyang Institute of Computing Technology, Chinese Academy of Science, China; Yugen Yi, Jiangxi Normal University, China |
| Session | BIO-8: Biological Image Analysis |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Biomedical Imaging and Signal Processing: [BIO-BIA] Biological image analysis |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Retinal vessel segmentation is a vital step for the diagnosis of many early eye-related diseases. In this work, we propose a new deep learning model, namely Channel Attention Residual U-Net (CAR-UNet), to accurately segment retinal vascular and non-vascular pixels. In this model, we introduced a novel Modified Efficient Channel Attention (MECA) to enhance the discriminative ability of the network by considering the interdependence between feature maps. On the one hand, we apply MECA to the "skip connections" in the traditional U-shaped networks, instead of simply copying the feature maps of the contracting path to the corresponding expansive path. On the other hand, we propose a Channel Attention Double Residual Block (CADRB), which integrates MECA into a residual structure as a core structure to construct the proposed CAR-UNet. The results show that our proposed CAR-UNet has reached the state-of-the-art performance on three publicly available retinal vessel datasets: DRIVE, CHASE DB1 and STARE. |
