ArticleRadiology. Artificial intelligence2020
Attention-Aware Discrimination for MR-to-CT Image Translation Using Cycle-Consistent Generative Adversarial Networks.
Article in Radiology. Artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Forecasting Alzheimer's disease progression via identity-preserved denoising diffusion generative adversarial network.NPJ digital medicine · 2026Article
- Article
- The intelligent development and preservation of folk sports culture under artificial intelligence.Scientific reports · 2025Article
- Generating synthetic brain PET images of synaptic density based on MR T1 images using deep learning.EJNMMI physics · 2025Article
- Article
- Improving the radiographic image analysis of the classic metaphyseal lesion via conditional diffusion models.Medical image analysis · 2024Article
- Automated cardiovascular MR myocardial scar quantification with unsupervised domain adaptation.European radiology experimental · 2024Article
- Mutual Information Guided Diffusion for Zero-Shot Cross-Modality Medical Image Translation.IEEE transactions on medical imaging · 2024Article
- Deep generative model of the distal tibial classic metaphyseal lesion in infants: assessment of synthetic images.Radiology advances · 2024Article
- Turning brain MRI into diagnostic PET:Medical image analysis · 2024Article
- The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance.Seminars in radiation oncology · 2024Review
- Synthetic computed tomography for low-field magnetic resonance-only radiotherapy in head-and-neck cancer using residual vision transformers.Physics and imaging in radiation oncology · 2023Article
- Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images.Diagnostics (Basel, Switzerland) · 2023Article
- Image Translation of Breast Ultrasound to Pseudo Anatomical Display by CycleGAN.Bioengineering (Basel, Switzerland) · 2023Article
- Multimodal image synthesis based on disentanglement representations of anatomical and modality specific features, learned using uncooperative relativistic GAN.Medical image analysis · 2022Article
- Improved accuracy of relative electron density and proton stopping power ratio through CycleGAN machine learning.Physics in medicine and biology · 2022Article
- Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey.Eye and vision (London, England) · 2022Review
- Detecting brain lesions in suspected acute ischemic stroke with CT-based synthetic MRI using generative adversarial networks.Annals of translational medicine · 2022Article
- Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices.Journal of magnetic resonance imaging : JMRI · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo suggest an attention-aware, cycle-consistent generative adversarial network (A-CycleGAN) enhanced with variational autoencoding (VAE) as a superior alternative to current state-of-the-art MR-to-CT image translation methods. MATERIALS AND
methodsAn attention-gating mechanism is incorporated into a discriminator network to encourage a more parsimonious use of network parameters, whereas VAE enhancement enables deeper discrimination architectures without inhibiting model convergence. Findings from 60 patients with head, neck, and brain cancer were used to train and validate A-CycleGAN, and findings from 30 patients were used for the holdout test set and were used to report final evaluation metric results using mean absolute error (MAE) and peak signal-to-noise ratio (PSNR).
resultsA-CycleGAN achieved superior results compared with U-Net, a generative adversarial network (GAN), and a cycle-consistent GAN. The A-CycleGAN averages, 95% confidence intervals (CIs), and Wilcoxon signed-rank two-sided test statistics are shown for MAE (19.61 [95% CI: 18.83, 20.39],
conclusionA-CycleGANs were a superior alternative to state-of-the-art MR-to-CT image translation methods.© RSNA, 2020.
Identifiers
What OpenQuestion holds
Registered trials
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.