Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution
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Abstract (arXiv)
We propose an image super resolution(ISR) method using generative adversarial networks (GANs) that takes a low resolution input fundus image and generates a high resolution super resolved (SR) image upto scaling factor of 16. This facilitates more accurate automated image analysis, especially for small or blurred landmarks and pathologies. Local saliency maps, which define each pixel's importance, are used to define a novel saliency loss in the GAN cost function. Experimental results show the resulting SR images have perceptual quality very close to the original images and perform better than competing methods that do not weigh pixels according to their importance. When used for retinal vasculature segmentation, our SR images result in accuracy levels close to those obtained when using the original images.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Oct 2017.
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Citing papers per year: 2021: 4; 2022: 4; 2023: 1.
9 papers.
- 2021Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentationcited by 22
- 2021CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysiscited by 8
- 2021Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentationcited by 5
- 2022Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classificationcited by 5
- 2021Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learningcited by 4
- 2022Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classificationcited by 2
- 2022Improved Super Resolution of MR Images Using CNNs and Vision Transformerscited by 1
- 2022Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysiscited by 0
- 2023Optimizing the AI Development Process by Providing the Best Support Environmentcited by 0
- 2023Optimizing the AI Development Process by Providing the Best Support Environment29 Apr 2023
- 2022Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis16 Nov 2022
- 2022Improved Super Resolution of MR Images Using CNNs and Vision Transformers24 Jul 2022
- 2022Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification27 Jun 2022
- 2022Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification4 Apr 2022
- 2021Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning15 Nov 2021
- 2021Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation1 Oct 2021
- 2021CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis15 Jun 2021
- 2021Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation13 Apr 2021