REFERENCES
1. Pelapur R, Prasath VBS, Moreno JC, Heck MM. 3D Workflow for segmentation and interactive visualization in brain MR images using multiphase active contours. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Kansas, USA; 2017. pp. 911-6.
2. Kalavathi P, Senthamilselvi M, Prasath VBS. Review of computational methods on brain symmetric and asymmetric analysis from neuroimaging techniques. Technologies 2017;5:16.
3. Kalavathi P, Prasath VBS. Automatic segmentation of cerebral hemispheres in MR human head scans. Int J Imaging Syst Technol 2016;26:15-23.
4. Kalavathi P, Prasath VBS. Adaptive nonlocal filtering for brain MRI restoration. In: Thampi S, Bandyopadhyay S, Krishnan S, Li KC, Mosin S, Ma M, editors. Advances in signal processing and intelligent recognition systems. Advances in intelligent systems and computing. Cham: Springer; 2015. pp. 571-80.
5. Greenspan H, van Ginneken B, Summers RM. Deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Medical Imag 2016;35:1153-9.
6. Yonekura A, Kawanaka H, Prasath, VBS, Aronow BJ, Takase H. Glioblastoma multiforme tissue histopathology images based disease stage classification with deep CNN. Proceedings of the 6th International Conference on Informatics, Electronics & Vision (ICIEV); 2017.
7. Yonekura A, Kawanaka H, Prasath, VBS, Aronow BJ, Takase, H. Improving the generalization of disease stage classification with deep CNN for glioma histopathological images. Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); Kansas, USA; 2017. pp. 1222-6.
8. Yonekura A, Kawanaka H, Prasath, VBS, Aronow BJ, Takase H. Disease stage classification for Glioblastoma Multiforme histopathological images using deep convolutional neural network. Proceedings of Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS); Otsu, Japan; 2017.
9. Vaidhya K. Unsupervised and semi-supervised deep learning for medical imaging. Anthill Inside. Madras, India: Predible Health; 2017.
10. Wang J, Fang Z, Lang N, Yuan H, Su MY, Baldi P. A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks. Comp Bio Med 2017;84:137-46.
11. Pang S, Yu Z, Orgun MA. A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images. Comp Meth Prog Biomed 2017;140:283-93.
12. Chyzhyk D, Savio A, Graña M. Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM. Neural Networks 2015;68:23-33.
13. Sarraf S, Tofighi G. Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data. Proceedings of Future Technologies Conference (FTC); 2016. pp. 816-20.
