The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging
Open accessAbstract
Artificial intelligence (AI) in medical imaging is a potentially disruptive technology. An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep learning is an essential foundation to weave design solutions that accommodate ethical and regulatory requirements, and to craft AI-based algorithms that enhance outcomes, quality, and efficiency. Moreover, a more holistic perspective of applications, opportunities, and challenges from a programmatic perspective contributes to ethical and sustainable implementation of AI solutions.
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Citations
Cites
(2016) · Cited by 11,679
Radiomics: extracting more information from medical images using advanced feature analysis
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2011.11.036 · Cited by 6,084
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968
Convolutional neural networks: an overview and application in radiology
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0639-9 · Cited by 4,682
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012
Thrall, J. H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K., & Brink, J. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.12.026 · Cited by 710
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology
Tang, A., Tam, R., Cadrin-Chênevert, A., Guest, W., Chong, J., Barfett, J., Chepelev, L., Cairns, R., Mitchell, J. R., Cicero, M. D., et al. (2018) · Canadian Association of Radiologists Journal · doi:10.1016/j.carj.2018.02.002 · Cited by 522
A gentle introduction to deep learning in medical image processing
Maier, A., Syben, C., Lasser, T., & Riess, C. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.12.003 · Cited by 515
Neural Networks and Deep Learning
(2016) · Computer Age Statistical Inference · doi:10.1017/cbo9781316576533.019 · Cited by 509
Cited by
Machine learning and deep learning approach for medical image analysis: diagnosis to detection
Rana, M., & Bhushan, M. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14305-w · Cited by 365
Herrmann, T., & Pfeiffer, S. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01391-5 · Cited by 173
“In the last column of the table, we also add the use of AI in medical image analysis (Currie et al. 2019).”
MethodFit‐for‐Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience
Godfrey, A., Vandendriessche, B., Bakker, J. P., Fitzer‐Attas, C., Gujar, N., Hobbs, M., Liu, Q., Northcott, C. A., Parks, V., Wood, W. A., et al. (2021) · Clinical and Translational Science · doi:10.1111/cts.12865 · Cited by 60
Currie, G. M., Hawk, K. E., & Rohren, E. M. (2025) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2024.05.005 · Cited by 52
Self-citationAn Approach to Binary Classification of Alzheimer’s Disease Using LSTM
Salehi, W., Baglat, P., Gupta, G., Khan, S. B., Almusharraf, A., Alqahtani, A., & Kumar, A. (2023) · Bioengineering · doi:10.3390/bioengineering10080950 · Cited by 42
Metadata
- Authors
- Currie, G., Hawk, K. E., Rohren, E., Vial, A., & Klein, R.
- Venue
- Journal of Medical Imaging and Radiation Sciences
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 50
- Issue
- 4
- Pages
- 477-487
- ISSN
- 1876-7982 (ISSN-L)
- 1939-8654
- OpenAlex
- W2972168296
- Cited by
- 452
- References
- 33
- FWCI
- 8.32
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Currie, G., Hawk, K. E., Rohren, E., Vial, A., & Klein, R. (2019). Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging. Journal of Medical Imaging and Radiation Sciences. https://doi.org/10.1016/j.jmir.2019.09.005
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
