mlsanityData to 14 Oct 2026

SA Unet Improved

arXiv 2308.15487 ↗First posted 18 Aug 2023eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Retinal vessels segmentation is well known problem in image processing on the medical field. Good segmentation may help doctors take better decisions while diagnose eyes disuses. This paper describes our work taking up the DRIVE challenge which include segmentation on retinal vessels. We invented a new method which combine using of StyleGAN2 and SA-Unet. Our innovation can help any small data set segmentation problem.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 12 references that are in the map. First posted on arXiv 18 Aug 2023.

Builds on

7 of 12 references.

References · 7

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  3. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  4. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  5. 2018Dual Attention Network for Scene Segmentationcited by 1,003
  6. 2020SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentationcited by 71
  7. 2019IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networkscited by 57

Built on by

Not cited yet.