ArticleMedical image analysis2019
Adversarial learning for mono- or multi-modal registration.
Article in Medical image analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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
35 citing papers in PubMed, 159 citations in OpenAlex.
- Review
- Hierarchical Multi-Scale Feature Fusion Network with Implicit Neural Representation and Mamba for Cross-Modality MRI Synthesis.Sensors (Basel, Switzerland) · 2026Article
- Toward general text-guided multimodal brain MRI synthesis for diagnosis and medical image analysis.Cell reports. Medicine · 2025Article
- Pano-GAN: A Deep Generative Model for Panoramic Dental Radiographs.Journal of imaging · 2025Article
- A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.Medical image analysis · 2025Review
- MTFN: multi-temporal feature fusing network with co-attention for DCE-MRI synthesis.BMC medical imaging · 2024Article
- Unsupervised deep learning registration model for multimodal brain images.Journal of applied clinical medical physics · 2023Article
- macJNet: weakly-supervised multimodal image deformable registration using joint learning framework and multi-sampling cascaded MIND.Biomedical engineering online · 2023Article
- A two-step deep learning method for 3DCT-2DUS kidney registration during breathing.Scientific reports · 2023Article
- DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT.Medical image analysis · 2023Article
- The Successive Next Network as Augmented Regularization for Deformable Brain MR Image Registration.Sensors (Basel, Switzerland) · 2023Article
- Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review.Sensors (Basel, Switzerland) · 2023Article
- Multi-mode information fusion navigation system for robot-assisted vascular interventional surgery.BMC surgery · 2023Article
- Artificial intelligence to predict outcomes of head and neck radiotherapy.Clinical and translational radiation oncology · 2023Article
- Cross-modal attention for multi-modal image registration.Medical image analysis · 2022Article
- Recent advances and clinical applications of deep learning in medical image analysis.Medical image analysis · 2022Review
- Unsupervised computed tomography and cone-beam computed tomography image registration using a dual attention network.Quantitative imaging in medicine and surgery · 2022Article
- Recurrent Tissue-Aware Network for Deformable Registration of Infant Brain MR Images.IEEE transactions on medical imaging · 2022Article
- Unsupervised Image Registration towards Enhancing Performance and Explainability in Cardiac and Brain Image Analysis.Sensors (Basel, Switzerland) · 2022Article
- Discussion on the possibility of multi-layer intelligent technologies to achieve the best recover of musculoskeletal injuries: Smart materials, variable structures, and intelligent therapeutic planning.Frontiers in bioengineering and biotechnology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
Abstract
This paper introduces an unsupervised adversarial similarity network for image registration. Unlike existing deep learning registration methods, our approach can train a deformable registration network without the need of ground-truth deformations and specific similarity metrics. We connect a registration network and a discrimination network with a deformable transformation layer. The registration network is trained with the feedback from the discrimination network, which is designed to judge whether a pair of registered images are sufficiently similar. Using adversarial training, the registration network is trained to predict deformations that are accurate enough to fool the discrimination network. The proposed method is thus a general registration framework, which can be applied for both mono-modal and multi-modal image registration. Experiments on four brain MRI datasets and a multi-modal pelvic image dataset indicate that our method yields promising registration performance in accuracy, efficiency and generalizability compared with state-of-the-art registration methods, including those based on deep learning.
Indexed as
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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.