ArticleBioMed research international2022
Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging.
Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Predicting pathological subtypes of pure ground-glass nodules using Swin Transformer deep learning model.Insights into imaging · 2025Article
- Special Issue: Artificial Intelligence in Advanced Medical Imaging.Bioengineering (Basel, Switzerland) · 2024Article
- An AI-Based Low-Risk Lung Health Image Visualization Framework Using LR-ULDCT.Journal of imaging informatics in medicine · 2024Article
- Automated diagnosis of adenoid hypertrophy with lateral cephalogram in children based on multi-scale local attention.Scientific reports · 2024Article
- Super-resolution techniques for biomedical applications and challenges.Biomedical engineering letters · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computerized tomography (CT) is widely used for clinical screening and treatment planning. In this study, we aimed to reduce X-ray radiation and achieve high-quality CT imaging by using low-intensity X-rays because CT radiation is damaging to the human body. An innovative vision transformer for medical image super-resolution (SR) is applied to establish a high-definition image target. To achieve this, we proposed a method called swin transformer and attention network (STAN) that uses the swin transformer network, which employs an attention method to overcome the long-range dependency difficulties encountered in CNNs and RNNs to enhance and restore the quality of medical CT images. We adopted the peak signal-to-noise ratio for performance comparison with other mainstream SR reconstruction models used in medical CT imaging. Experimental results revealed that the proposed STAN model yields superior medical CT imaging results than the existing SR techniques based on CNNs. The proposed STAN model employs a self-attention mechanism to more effectively extract critical features and long-range information, hence enhancing the quality of medical CT image reconstruction.
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Registered trials
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