Page 301 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications
P. 301
292 Chapter 10 Deep neural network in medical image processing
[37] T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, et al., MXNet: A Flexible
and Efficient Machine Learning Library for Heterogeneous Distributed
Systems, 2015, pp. 1e6, arXiv:1512.01274, http://arxiv.org/abs/1512.01274.
[38] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, et al.,
Tensorflow: Large-Scale Machine Learning on Heterogeneous Distributed
Systems, 2015, http://download.tensorflow.org/paper/whitepaper2015.pdf.
[39] J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, et
al., Theano: a {CPU} and {GPU} math expression compiler, in: Proceedings
of the Python for Scientific Computing Conference ({SciPy}) (Scipy), 2010,
pp. 1e7.
[40] A. Taghanaki, et al., Deep semantic segmentation of natural and medical
images: a review, Artifi. Intell. Rev. Deep (2020), https://doi.org/10.1007/
s10462-020-09854-1.
[41] C. Nicholas, et al., Accuracy assessment measures for object-based Image
segmentation goodness, Photogramm. Eng. Remote Sens (2010).
[42] J. Wang, et al., Image and video segmentation by anisotropic kernel mean
shift, European Conf. Comput. Vision (2004).
[43] M. Neubert, et al., Assessing image segmentation quality e concepts,
methods and application, Object-Based Image Analysis, Springer, 2008.
[44] E. Basaeed, A supervised hierarchical segmentation of remote-sensing
images using a committee of multi-scale convolutional neural networks, Int.
J. Remote Sens (2016).
[45] R. Adams, et al., Seeded region growing, EEE Trans. Pattern Anal. Mach.
Intell. (1994).
[46] L.L.F. Janssen, et al., Terrain objects, their dynamics and their monitoring
by the integration of GIS and remote sensing, IEEE Trans. Geosci. Remote
Sens. (1995).
[47] W.S. McCulloch, W.A. Pitts, A logical calculus of the ideas immanent in
nervous activity, Bull. Math Biophys. (1943).