# Channel Attention Residual U-Net for Retinal Vessel Segmentation

arXiv: https://arxiv.org/abs/2004.03702 · HTML: https://mlsanity.com/paper/2004.03702/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [Nested Multiple Instance Learning with Attention Mechanisms](https://mlsanity.com/paper/2111.00947/) (2021)
- [DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet](https://mlsanity.com/paper/2111.04739/) (2021)
- [SPIRONet: Spatial-Frequency Learning and Graph-based Channel Interaction Network for Vessel Segmentation](https://mlsanity.com/paper/2406.19749/) (2024)
- [PAENet: A Progressive Attention-Enhanced Network for 3D to 2D Retinal Vessel Segmentation](https://mlsanity.com/paper/2108.11695/) (2021)
- [Residual Channel Attention Network for Brain Glioma Segmentation](https://mlsanity.com/paper/2205.10758/) (2022)
- [A novel attention mechanism for noise-adaptive and robust segmentation of microtubules in microscopy images](https://mlsanity.com/paper/2507.07800/) (2025)
- [MTFlow: Time-Conditioned Flow Matching for Microtubule Segmentation in Noisy Microscopy Images](https://mlsanity.com/paper/2601.14841/) (2026)
- [CurvSegFlow: Time-Conditioned Flow Matching for Robust Segmentation of Curvilinear Structures in Noisy Biomedical Images](https://mlsanity.com/paper/2606.21608/) (2026)
- [(M)SLAe-Net: Multi-Scale Multi-Level Attention embedded Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2109.02084/) (2021)
- [Channel Attention Separable Convolution Network for Skin Lesion Segmentation](https://mlsanity.com/paper/2309.01072/) (2023)
- [KaLDeX: Kalman Filter based Linear Deformable Cross Attention for Retina Vessel Segmentation](https://mlsanity.com/paper/2410.21160/) (2024)

## Abstract (arXiv)

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.