ArticleIEEE transactions on medical imaging2020
CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE).
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 1 of them a synthesis that pooled it.
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Who cites it
106 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation.Journal of digital imaging · 2022Pooled it
- Super-Resolution MR Spectroscopic Imaging via Diffusion Models for Tumor Metabolism Mapping.Journal of imaging informatics in medicine · 2026Article
- Super-resolution and habitat radiomics based computed tomography machine-learning model for prediction of lung invasive adenocarcinoma: a multi-centre study.Quantitative imaging in medicine and surgery · 2026Article
- Synthetic artificial intelligence in cardiology: from generative models to clinical applications.European heart journal open · 2026Review
- Comparison of conventional 512-matrix CT images with Swin2SR-based 2048-matrix images in the visualization and diagnosis of lung nodules.Japanese journal of radiology · 2026Article
- Enhancing low-dose CT denoising via multi-view knowledge transfer without paired data.Frontiers in radiology · 2026Article
- Non-invasive evaluation of muscle invasion and survival prognosis in bladder cancer using enhanced CT-based deep learning radiomics: a multi-center real-world cohort study.Military Medical Research · 2026Article
- From preoperative computed tomography to postmastoidectomy mesh construction: mastoidectomy shape prediction for cochlear implant surgery.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- A review of image processing and analysis of computed tomography images using deep learning methods.Physical and engineering sciences in medicine · 2025Review
- MSFANet: A Multi-Scale Feature Fusion Transformer with Hybrid Attention for Remote Sensing Image Super-Resolution.Sensors (Basel, Switzerland) · 2025Article
- Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization.Nanophotonics (Berlin, Germany) · 2025Review
- Simulating Sinogram-Domain Motion and Correcting Image-Domain Artifacts Using Deep Learning in HR-pQCT Bone Imaging.IEEE transactions on radiation and plasma medical sciences · 2025Article
- A Multi-Model Image Enhancement and Tailored U-Net Architecture for Robust Diabetic Retinopathy Grading.Diagnostics (Basel, Switzerland) · 2025Article
- Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction.ArXiv · 2025Article
- Can super resolution via deep learning improve classification accuracy in dental radiography?Dento maxillo facial radiology · 2025Article
- Continuous representation-based reconstruction for computed tomography.Medical physics · 2025Article
- Efficient sparse-view medical image classification for low radiation and rapid COVID-19 diagnosis.Biomedical engineering letters · 2025Article
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- [A low-dose CT image restoration method based on central guidance and alternating optimization].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- A Multi-Modal Light Sheet Microscope for High-Resolution 3D Tomographic Imaging with Enhanced Raman Scattering and Computational Denoising.Sensors (Basel, Switzerland) · 2025Article
46 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors.
Funding
Abstract
In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel 1×1 CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.