The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Machine Learning in Medical Imaging
Open accessAbstract
This article will discuss very different ways of using machine learning that may be less familiar, and we will demonstrate through examples the role of these concepts in medical imaging. Although the term machine learning is relatively recent, the ideas of machine learning have been applied to medical imaging for decades, perhaps most notably in the areas of computer-aided diagnosis (CAD) and functional brain mapping. We will not attempt in this brief article to survey the rich literature of this field. Instead our goals will be 1) to acquaint the reader with some modern techniques that are now staples of the machine-learning field and 2) to illustrate how these techniques can be employed in various ways in medical imaging.
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Citations
Cites
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Investigations into resting-state connectivity using independent component analysis
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1634 · Cited by 3,398
Machine learning classifiers and fMRI: A tutorial overview
Pereira, F., Mitchell, T., & Botvinick, M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.11.007 · Cited by 1,624
Evaluating fMRI preprocessing pipelines
Strother, S. C. (2006) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2006.1607667 · Cited by 169
Self-citation
Cited by
Current Applications and Future Impact of Machine Learning in Radiology
Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A. E., Pianykh, O. S., Geis, J. R., Pandharipande, P. V., Brink, J. A., & Dreyer, K. J. (2018) · Radiology · doi:10.1148/radiol.2018171820 · Cited by 800
Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine
Adir, O., Poley, M., Chen, G., Froim, S., Krinsky, N., Shklover, J., Shainsky‐Roitman, J., Lammers, T., & Schroeder, A. (2020) · Advanced Materials · doi:10.1002/adma.201901989 · Cited by 496
Deep learning in microscopy image analysis: a survey
Xing, F., Xie, Y., Su, H., Liu, F., & Yang, L. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2766168 · Cited by 460
Artificial intelligence, machine learning and deep learning
Ongsulee, P. (2017) · 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE) · doi:10.1109/ictke.2017.8259629 · Cited by 367
Wolterink, J. M., Leiner, T., de Vos, B. D., van Hamersvelt, R. W., Viergever, M. A., & Išgum, I. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.04.004 · Cited by 321
Metadata
- Authors
- Wernick, M., Yang, Y., Brankov, J., Yourganov, G., & Strother, S.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2010-07
- Volume
- 27
- Issue
- 4
- Pages
- 25-38
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W2045732268
- Cited by
- 345
- References
- 4
- FWCI
- 3.69
- Type
- journal article
- Language
- en
- Open access
- green
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- Wernick, M., Yang, Y., Brankov, J., Yourganov, G., & Strother, S. (2010). Machine Learning in Medical Imaging. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2010.936730
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