CGDD-Net: Context-Guided Dynamic Detail Modeling for Retinal Vessel Segmentation
cs.CV
Submitted: 2026-10-04
Updated: 2026-10-04
Code: https://github.com/lixincheng-xcl/CGDD-Net
Sources
- U-Net: Convolutional Networks for Biomedical Image Segmentation
- CE-Net: Context Encoder Network for 2D Medical Image Segmentation
- LadderNet: Multi-path networks based on U-Net for medical image segmentation
- IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
- CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging
- Deformable Convolutional Networks
- Vision Transformer with Deformable Attention
- DPN: Detail-Preserving Network with High Resolution Representation for Efficient Segmentation of Retinal Vessels
- SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
- FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures
- Novel Extraction of Discriminative Fine-Grained Feature to Improve Retinal Vessel Segmentation
- M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments
- UNet++: A Nested U-Net Architecture for Medical Image Segmentation
- UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
- Attention U-Net: Learning Where to Look for the Pancreas
- Selective Kernel Networks
- Dynamic Convolution: Attention over Convolution Kernels
- clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
- Adam: A Method for Stochastic Optimization
- SGDR: Stochastic Gradient Descent with Warm Restarts
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