The DOI matches its Crossref record; 147 of the 152 references it cites were found in a registry or an index.
A scoping review of transfer learning research on medical image analysis using ImageNet
Open accessAbstract
Objective Employing transfer learning (TL) with convolutional neural networks (CNNs), well-trained on non-medical ImageNet dataset, has shown promising results for medical image analysis in recent years. We aimed to conduct a scoping review to identify these studies and summarize their characteristics in terms of the problem description, input, methodology, and outcome. Materials and methods To identify relevant studies, MEDLINE, IEEE, and ACM digital library were searched for studies published between June 1st, 2012 and January 2nd, 2020. Two investigators independently reviewed articles to determine eligibility and to extract data according to a study protocol defined a priori. Results After screening of 8421 articles, 102 met the inclusion criteria. Of 22 anatomical areas, eye (18%), breast (14%), and brain (12%) were the most commonly studied. Data augmentation was performed in 72% of fine-tuning TL studies versus 15% of the feature-extracting TL studies. Inception models were the most commonly used in breast related studies (50%), while VGGNet was the common in eye (44%), skin (50%) and tooth (57%) studies. AlexNet for brain (42%) and DenseNet for lung studies (38%) were the most frequently used models. Inception models were the most frequently used for studies that analyzed ultrasound (55%), endoscopy (57%), and skeletal system X-rays (57%). VGGNet was the most common for fundus (42%) and optical coherence tomography images (50%). AlexNet was the most frequent model for brain MRIs (36%) and breast X-Rays (50%). 35% of the studies compared their model with other well-trained CNN models and 33% of them provided visualization for interpretation. Discussion This study identified the most prevalent tracks of implementation in the literature for data preparation, methodology selection and output evaluation for various medical image analysis tasks. Also, we identified several critical research gaps existing in the TL studies on medical image analysis. The findings of this scoping review can be used in future TL studies to guide the selection of appropriate research approaches, as well as identify research gaps and opportunities for innovation.
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation
Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., et al. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0850 · Cited by 43,565
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Scoping studies: towards a methodological framework
Arksey, H., & O'Malley, L. (2005) · International Journal of Social Research Methodology · doi:10.1080/1364557032000119616 · Cited by 36,742
(2014) · Cited by 33,419
Research works printed but not linked: 1
- Cheplygina, 2019, Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis, Med. Image Anal., 54, 280, 10.1016/j.media.2019.03.009 · DOI · Paper page
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Sun, S., Huang, J., Ma, B., Liu, Z., & Liu, M. X. (2026) · Advanced ultrasound in diagnosis and therapy · doi:10.26599/audt.2026.260033
Benhenneda, R., Brouard, T., Samargandi, R., Charousset, C., & Berhouet, J. (2026) · Orthopaedics & Traumatology Surgery & Research · doi:10.1016/j.otsr.2026.104933
Lee, S.-H., Vaitheeshwari, R., Kang, C.-W., Liu, L., Chen, C.-C., Yeh, C.-H., Wu, E. H., & Yeh, S. (2026) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2026.3744907
Araújo, T., Schaan, B. D., Freitas, C. M. D. S., & Navaux, P. O. A. (2026) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2026.105371
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Deep Learning for Pneumonia Prediction with ResNet50 and DenseNet121
Monisha, S., & Sridevi, S. (2026) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici69824.2026.11724126
Kant, V., Aluvala, S., Bhatt, S., Joshi, A., Mahajan, R., & Srivastav, A. (2026) · doi:10.1109/incect72370.2026.11742742
Metadata
- Authors
- Morid, M. A., Borjali, A., & Del Fiol, G.
- Affiliations
- Santa Clara University (US)
- Harvard University (US)
- Corresponding author
- Morid, Mohammad Amin
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 128
- Pages
- 104115
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W3020996329
- Cited by
- 508
- References
- 152
- Citations by year
- 2026: 68 · 2025: 92 · 2024: 113 · 2023: 107 · 2022: 81 · 2021: 42
- FWCI
- 4.43
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- transfer learning, medical image analysis, ImageNet, convolutional neural networks, fine-tuning, feature extraction, data augmentation, Inception model, VGG, AlexNet, DenseNet, ocular imaging, breast imaging, brain MRI, ultrasound, endoscopy, optical coherence tomography, fundus images, research gaps
- MeSH terms
- Machine Learning, Mammography, Tooth, Neural Networks, Computer
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