ArticleJournal of healthcare engineering2017
Deep Learning- and Transfer Learning-Based Super Resolution Reconstruction from Single Medical Image.
Article in Journal of healthcare engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- AI-Enhanced Super-Resolution Handheld Ultrasound for Carotid Plaque Detection in Community Screening.Annals of family medicine · 2026Article
- Exploring cross-regional and cross-variable transferability of a ResNet-based super-resolution method for the ERA5 data.Scientific reports · 2026Article
- Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.Chinese medicine · 2026Review
- The Efficacy of a Super-Resolution Reconstruction Radiomics Model Based on T2WI for Predicting Placenta Accreta Spectrum Disorders: A Multicenter Study.Current medical imaging · 2026Article
- Multiphase Reconstruction of Heterogeneous Materials Using Machine Learning and Quality of Connection Function.Materials (Basel, Switzerland) · 2024Article
- Detection of spatial avoidance between sousliks and moles by combining field observations, remote sensing and deep learning techniques.Scientific reports · 2022Article
- Super-Resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network.Sensors (Basel, Switzerland) · 2022Article
- Single-image reconstruction using novel super-resolution technique for large-scaled images.Soft computing · 2022Article
- Do Radiographic Assessments of Periodontal Bone Loss Improve with Deep Learning Methods for Enhanced Image Resolution?Sensors (Basel, Switzerland) · 2021Article
- Automatic Cephalometric Landmark Identification System Based on the Multi-Stage Convolutional Neural Networks with CBCT Combination Images.Sensors (Basel, Switzerland) · 2021Article
- A comprehensive review of deep learning-based single image super-resolution.PeerJ. Computer science · 2021Article
- Efficient Automated Disease Diagnosis Using Machine Learning Models.Journal of healthcare engineering · 2021Article
- The role of convolutional neural networks in scanning probe microscopy: a review.Beilstein journal of nanotechnology · 2021Review
- Super resolution for root imaging.Applications in plant sciences · 2020Article
- Application of a Modified Generative Adversarial Network in the Superresolution Reconstruction of Ancient Murals.Computational intelligence and neuroscience · 2020Article
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Authors and funding
2 authors.
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Abstract
Medical images play an important role in medical diagnosis and research. In this paper, a transfer learning- and deep learning-based super resolution reconstruction method is introduced. The proposed method contains one bicubic interpolation template layer and two convolutional layers. The bicubic interpolation template layer is prefixed by mathematics deduction, and two convolutional layers learn from training samples. For saving training medical images, a SIFT feature-based transfer learning method is proposed. Not only can medical images be used to train the proposed method, but also other types of images can be added into training dataset selectively. In empirical experiments, results of eight distinctive medical images show improvement of image quality and time reduction. Further, the proposed method also produces slightly sharper edges than other deep learning approaches in less time and it is projected that the hybrid architecture of prefixed template layer and unfixed hidden layers has potentials in other applications.
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