ReviewBiomedical engineering letters2024
Super-resolution techniques for biomedical applications and challenges.
Review in Biomedical engineering letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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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.
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Who cites it
10 citing papers in PubMed.
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- Endothelial Continuum and Capillary Specialization in Pulmonary Vascular Development.Arteriosclerosis, thrombosis, and vascular biology · 2026Review
- An interactive cascaded deep learning framework with expert refinement for accurate striatal subregion segmentation.Scientific reports · 2026Article
- Ultrastructure Expansion Microscopy for the Analysis of Trypanosoma cruzi Infection.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Light-RepViTSR: ultra-lightweight super-resolution for real-time photoacoustic endoscopy in tumor biopsy.Frontiers in bioengineering and biotechnology · 2026Article
- Laser Ultrasound Super-Resolution Imaging for Multi-Parametric Non-Invasive Volumetric Characterization of Brain Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Deep learning-based super-resolution method for projection image compression in radiotherapy.Quantitative imaging in medicine and surgery · 2025Article
- Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women.BMC medical imaging · 2025Article
- Fusing residual dense and attention in generative adversarial networks for super-resolution of medical images.Quantitative imaging in medicine and surgery · 2025Article
- Feasibility study of single-image super-resolution scanning system based on deep learning for pathological diagnosis of oral epithelial dysplasia.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Super-resolution (SR) techniques have revolutionized the field of biomedical applications by detailing the structures at resolutions beyond the limits of imaging or measuring tools. These techniques have been applied in various biomedical applications, including microscopy, magnetic resonance imaging (MRI), computed tomography (CT), X-ray, electroencephalogram (EEG), ultrasound, etc. SR methods are categorized into two main types: traditional non-learning-based methods and modern learning-based approaches. In both applications, SR methodologies have been effectively utilized on biomedical images, enhancing the visualization of complex biological structures. Additionally, these methods have been employed on biomedical data, leading to improvements in computational precision and efficiency for biomedical simulations. The use of SR techniques has resulted in more detailed and accurate analyses in diagnostics and research, essential for early disease detection and treatment planning. However, challenges such as computational demands, data interpretation complexities, and the lack of unified high-quality data persist. The article emphasizes these issues, underscoring the need for ongoing development in SR technologies to further improve biomedical research and patient care outcomes.
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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 Socratic graph.