publication . Preprint . 2018

Wall Stress Estimation of Cerebral Aneurysm based on Zernike Convolutional Neural Networks

Sun, Zhiyu; Lu, Jia; Baek, Stephen;
Open Access English
  • Published: 20 Jun 2018
Abstract
Comment: 10 pages
Subjects
free text keywords: Computer Science - Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
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19 references, page 1 of 2

[2] Mathieu Andreux, Emanuele Rodola, Mathieu Aubry, and Daniel Cremers. Anisotropic laplacebeltrami operators for shape analysis. In European Conference on Computer Vision, pages 299-312. Springer, 2014.

[3] Davide Boscaini, Jonathan Masci, Simone Melzi, Michael M Bronstein, Umberto Castellani, and Pierre Vandergheynst. Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks. In Computer Graphics Forum, volume 34, pages 13-23. Wiley Online Library, 2015.

[4] Davide Boscaini, Jonathan Masci, Emanuele Rodolà, and Michael Bronstein. Learning shape correspondence with anisotropic convolutional neural networks. In Advances in Neural Information Processing Systems, pages 3189-3197, 2016. [OpenAIRE]

[5] Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael M Bronstein, and Daniel Cremers. Anisotropic diffusion descriptors. In Computer Graphics Forum, volume 35, pages 431-441. Wiley Online Library, 2016.

[6] Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 34(4):18-42, 2017.

[7] Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203, 2013.

[8] François Chollet et al. Keras. https://github.com/keras-team/keras, 2015.

[9] Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems, pages 3844-3852, 2016. [OpenAIRE]

[10] David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams. Convolutional networks on graphs for learning molecular fingerprints. In Advances in neural information processing systems, pages 2224- 2232, 2015. [OpenAIRE]

[11] Paul Fricker. Analyzing lasik optical data using zernike functions. Matlab Digest, 1, 2008.

[12] Yotam Hechtlinger, Purvasha Chakravarti, and Jining Qin. A generalization of convolutional neural networks to graph-structured data. arXiv preprint arXiv:1704.08165, 2017. [OpenAIRE]

[13] Mikael Henaff, Joan Bruna, and Yann LeCun. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163, 2015.

[18] Hao Li, Robert W. Sumner, and Mark Pauly. Global correspondence optimization for non-rigid registration of depth scans. Computer Graphics Forum (Proc. SGP'08), 27(5), July 2008.

[19] Jonathan Masci, Davide Boscaini, Michael Bronstein, and Pierre Vandergheynst. Geodesic convolutional neural networks on riemannian manifolds. In Proceedings of the IEEE international conference on computer vision workshops, pages 37-45, 2015.

[20] Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein. Geometric deep learning on graphs and manifolds using mixture model cnns. In Proc. CVPR, volume 1, page 3, 2017.

19 references, page 1 of 2
Abstract
Comment: 10 pages
Subjects
free text keywords: Computer Science - Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Related Organizations
Download from
19 references, page 1 of 2

[2] Mathieu Andreux, Emanuele Rodola, Mathieu Aubry, and Daniel Cremers. Anisotropic laplacebeltrami operators for shape analysis. In European Conference on Computer Vision, pages 299-312. Springer, 2014.

[3] Davide Boscaini, Jonathan Masci, Simone Melzi, Michael M Bronstein, Umberto Castellani, and Pierre Vandergheynst. Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks. In Computer Graphics Forum, volume 34, pages 13-23. Wiley Online Library, 2015.

[4] Davide Boscaini, Jonathan Masci, Emanuele Rodolà, and Michael Bronstein. Learning shape correspondence with anisotropic convolutional neural networks. In Advances in Neural Information Processing Systems, pages 3189-3197, 2016. [OpenAIRE]

[5] Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael M Bronstein, and Daniel Cremers. Anisotropic diffusion descriptors. In Computer Graphics Forum, volume 35, pages 431-441. Wiley Online Library, 2016.

[6] Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 34(4):18-42, 2017.

[7] Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203, 2013.

[8] François Chollet et al. Keras. https://github.com/keras-team/keras, 2015.

[9] Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems, pages 3844-3852, 2016. [OpenAIRE]

[10] David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams. Convolutional networks on graphs for learning molecular fingerprints. In Advances in neural information processing systems, pages 2224- 2232, 2015. [OpenAIRE]

[11] Paul Fricker. Analyzing lasik optical data using zernike functions. Matlab Digest, 1, 2008.

[12] Yotam Hechtlinger, Purvasha Chakravarti, and Jining Qin. A generalization of convolutional neural networks to graph-structured data. arXiv preprint arXiv:1704.08165, 2017. [OpenAIRE]

[13] Mikael Henaff, Joan Bruna, and Yann LeCun. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163, 2015.

[18] Hao Li, Robert W. Sumner, and Mark Pauly. Global correspondence optimization for non-rigid registration of depth scans. Computer Graphics Forum (Proc. SGP'08), 27(5), July 2008.

[19] Jonathan Masci, Davide Boscaini, Michael Bronstein, and Pierre Vandergheynst. Geodesic convolutional neural networks on riemannian manifolds. In Proceedings of the IEEE international conference on computer vision workshops, pages 37-45, 2015.

[20] Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein. Geometric deep learning on graphs and manifolds using mixture model cnns. In Proc. CVPR, volume 1, page 3, 2017.

19 references, page 1 of 2
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