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Journal of Healthcare Engineering
Volume 2019, Article ID 9874591, 2 pages
https://doi.org/10.1155/2019/9874591
Editorial

Machine Learning for Medical Imaging

1Amazon.com, Cambridge, USA
2Icahn School of Medicine at Mount Sinai, New York City, USA
3Dalian University of Technology, Dalian, China
4Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA

Correspondence should be addressed to Geng-Shen Fu; ude.cbmu@1sgneguf

Received 20 March 2019; Accepted 20 March 2019; Published 28 April 2019

Copyright © 2019 Geng-Shen Fu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, Berlin, Germany, 2006.
  2. E. Brynjolfsson and T. Mitchell, “What can machine learning do? Workforce implications,” Science, vol. 358, no. 6370, pp. 1530–1534, 2017. View at Publisher · View at Google Scholar
  3. G. Hinton, L. Deng, D. Yu et al., “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82–97, 2012. View at Publisher · View at Google Scholar
  4. M. Ibnkahla, “Applications of neural networks to digital communications—a survey,” Signal Processing, vol. 80, no. 7, pp. 1185–1215, 2000. View at Publisher · View at Google Scholar
  5. X. Chen, Z. J. Wang, and M. McKeown, “Joint blind source separation for neurophysiological data analysis: multiset and multimodal methods,” IEEE Signal Processing Magazine, vol. 33, no. 3, pp. 86–107, 2016. View at Publisher · View at Google Scholar
  6. V. D. Calhoun, J. Liu, and T. Adalı, “A review of group ICA for FMRI data and ICA for joint inference of imaging, genetic, and ERP data,” NeuroImage, vol. 45, no. 1, pp. S163–S172, 2008. View at Publisher · View at Google Scholar
  7. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 28, pp. 436–444, 2015. View at Publisher · View at Google Scholar
  8. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, 2017. View at Publisher · View at Google Scholar
  9. H.-C. Shin, H. R. Roth, M. Gao et al., “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1285–1298, 2016. View at Publisher · View at Google Scholar
  10. S. Pereira, A. Pinto, V. Alves, and C. A. Silva, “Brain tumor segmentation using convolutional neural networks in MRI images,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1240–1251, 2016. View at Publisher · View at Google Scholar
  11. X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, vol. 15, no. 11–13, pp. 315–323, Fort Lauderdale, FL, USA, April 2011.
  12. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, Las Vegas, NV, USA, June 2016.