# Attention Guided Network for Retinal Image Segmentation

arXiv: https://arxiv.org/abs/1907.12930 · HTML: https://mlsanity.com/paper/1907.12930/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2019.

## Built on by (most cited first, 30 of 32)

- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images](https://mlsanity.com/paper/2211.01607/) (2022)
- [AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images](https://mlsanity.com/paper/2204.12077/) (2022)
- [Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network](https://mlsanity.com/paper/2111.03216/) (2021)
- [RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging](https://mlsanity.com/paper/2203.14928/) (2022)
- [DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences](https://mlsanity.com/paper/2306.12153/) (2023)
- [3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework](https://mlsanity.com/paper/2107.12046/) (2021)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [Learning self-calibrated optic disc and cup segmentation from multi-rater annotations](https://mlsanity.com/paper/2206.05092/) (2022)
- [Generative appearance replay for continual unsupervised domain adaptation](https://mlsanity.com/paper/2301.01211/) (2023)
- [DSCA: A Digital Subtraction Angiography Sequence Dataset and Spatio-Temporal Model for Cerebral Artery Segmentation](https://mlsanity.com/paper/2406.00341/) (2024)
- [Bridging Synthetic and Real Images: a Transferable and Multiple Consistency aided Fundus Image Enhancement Framework](https://mlsanity.com/paper/2302.11795/) (2023)
- [Denoising Diffusion Probabilistic Model for Retinal Image Generation and Segmentation](https://mlsanity.com/paper/2308.08339/) (2023)
- [TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesion Segmentation](https://mlsanity.com/paper/2408.09687/) (2024)
- [Retinal Vessel Segmentation with Pixel-wise Adaptive Filters](https://mlsanity.com/paper/2202.01782/) (2022)
- [Opinions Vary? Diagnosis First!](https://mlsanity.com/paper/2202.06505/) (2022)
- [Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle](https://mlsanity.com/paper/2208.03016/) (2022)
- [Full-scale Representation Guided Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2501.18921/) (2025)
- [MoNetV2: Enhanced Motion Network for Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2506.15835/) (2025)
- [SPNet: A novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss](https://mlsanity.com/paper/2202.09515/) (2022)
- [Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters](https://mlsanity.com/paper/2212.00601/) (2022)
- [Attention W-Net: Improved Skip Connections for better Representations](https://mlsanity.com/paper/2110.08811/) (2021)
- [GAEI-UNet: Global Attention and Elastic Interaction U-Net for Vessel Image Segmentation](https://mlsanity.com/paper/2308.08345/) (2023)
- [Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2308.16573/) (2023)
- [SA-UNetv2: Rethinking Spatial Attention U-Net for Retinal Vessel Segmentation](https://mlsanity.com/paper/2509.11774/) (2025)
- [Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy](https://mlsanity.com/paper/2105.09737/) (2021)
- [Exploring the Effects of Data Augmentation for Drivable Area Segmentation](https://mlsanity.com/paper/2208.03437/) (2022)
- [Overview of Deep Learning Methods for Retinal Vessel Segmentation](https://mlsanity.com/paper/2306.06116/) (2023)
- [BAAF: A benchmark attention adaptive framework for medical ultrasound image segmentation tasks](https://mlsanity.com/paper/2310.00919/) (2023)
- [Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures](https://mlsanity.com/paper/2406.13128/) (2024)

## Abstract (arXiv)

Learning structural information is critical for producing an ideal result in retinal image segmentation. Recently, convolutional neural networks have shown a powerful ability to extract effective representations. However, convolutional and pooling operations filter out some useful structural information. In this paper, we propose an Attention Guided Network (AG-Net) to preserve the structural information and guide the expanding operation. In our AG-Net, the guided filter is exploited as a structure sensitive expanding path to transfer structural information from previous feature maps, and an attention block is introduced to exclude the noise and reduce the negative influence of background further. The extensive experiments on two retinal image segmentation tasks (i.e., blood vessel segmentation, optic disc and cup segmentation) demonstrate the effectiveness of our proposed method.