Progressive Generative Adversarial Networks for Medical Image Super resolution
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Abstract (arXiv)
Anatomical landmark segmentation and pathology localization are important steps in automated analysis of medical images. They are particularly challenging when the anatomy or pathology is small, as in retinal images and cardiac MRI, or when the image is of low quality due to device acquisition parameters as in magnetic resonance (MR) scanners. We propose an image super-resolution method using progressive generative adversarial networks (P-GAN) that can take as input a low-resolution image and generate a high resolution image of desired scaling factor. The super resolved images can be used for more accurate detection of landmarks and pathology. Our primary contribution is in proposing a multistage model where the output image quality of one stage is progressively improved in the next stage by using a triplet loss function. The triplet loss enables stepwise image quality improvement by using the output of the previous stage as the baseline. This facilitates generation of super resolved images of high scaling factor while maintaining good image quality. Experimental results for image super-resolution show that our proposed multistage P-GAN outperforms competing methods and baseline GAN.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Feb 2019.
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Citing papers per year: 2021: 6; 2022: 5.
11 papers.
- 2021Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptationcited by 24
- 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
- 2021Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rayscited by 2
- 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
- 2022SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolutioncited by 0
- 2022SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution22 Nov 2022
- 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
- 2021Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation5 Mar 2021
- 2021Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays19 Feb 2021