Deep Learning vs. Conventional Machine Learning: Pilot Study of WMH Segmentation in Brain MRI with Absence or Mild Vascular Pathology

@article{Rachmadi2017DeepLV,
  title={Deep Learning vs. Conventional Machine Learning: Pilot Study of WMH Segmentation in Brain MRI with Absence or Mild Vascular Pathology},
  author={Muhammad Febrian Rachmadi and Maria del C. Vald{\'e}s Hern{\'a}ndez and Maria Leonora Fatimah Agan and Taku Komura},
  journal={J. Imaging},
  year={2017},
  volume={3},
  pages={66}
}
In the wake of the use of deep learning algorithms in medical image analysis, we compared performance of deep learning algorithms, namely the deep Boltzmann machine (DBM), convolutional encoder network (CEN) and patch-wise convolutional neural network (patch-CNN), with two conventional machine learning schemes: Support vector machine (SVM) and random forest (RF), for white matter hyperintensities (WMH) segmentation on brain MRI with mild or no vascular pathology. We also compared all these… CONTINUE READING
Recent Discussions
This paper has been referenced on Twitter 65 times over the past 90 days. VIEW TWEETS

References

Publications referenced by this paper.
Showing 1-10 of 46 references

DeepMedic. Available online: https://biomedia.doc.ic.ac.uk/software/ deepmedic/ (accessed on

  • K. Kamnitsas, B. Glocker
  • 2016
Highly Influential
5 Excerpts

Similar Papers

Loading similar papers…