[8]
Briot JP, Pachet F. Music generation by deep learning-challenges and directions. arXiv preprint 1712.
[9]
Briot JP, Hadjeres G, Pachet FD. Deep learningtechniques for music generation- A survey. arXiv preprint 1709.
[13]
Moeskops P, Wolterink JM, van der Velden BH, Gilhuijs KG, Leiner T, Viergever MA. Išgum Deep learning for multi-task medical image segmentation in multiple modalities. International Conference on Medical Image Computing and Computer-Assisted Intervention. 2016 Oct 17-21; Athens, Greece: Springer 2016.
[15]
Rathi VG, Palani S. Brain tumor detection and classification using deep learning classifier on MRI images. Res J Appl Sci Eng Technol 2015; 10(2): 177-87.
[23]
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014; 15(1): 1929-58.
[30]
Milletari F, Navab N, Ahmadi SA. V-net: Fully convolutional neural networks for volumetric medical image segmentation. Fourth international conference on 3D vision (3DV). Oct 25; IEEE 2016; pp. 565-71.
[50]
Wang G, Li W, Ourselin S, Vercauteren T. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. International MICCAI brainlesion workshop 2017; 178-90.
[53]
Dolz J, Desrosiers C, Ayed IB. IVD-Net: Intervertebral disc localization and segmentation in MRI with a multi-modal UNet. International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging. Sep 19-21; China: Springer 2018.
[55]
Kamnitsas K, Bai W, Ferrante E, et al. Ensembles of multiple models and architectures for robust brain tumour segmentation. International MICCAI Brainlesion Workshop. 2017 Sep 14-16; 2017.
[56]
Myronenko A. 3D MRI brain tumor segmentation using autoencoder regularization. International MICCAI Brainlesion Workshop. 2017 Sep 16-17; China: Springer 2018.
[58]
Bui TD, Shin J, Moon T. 3d densely convolutional networks for volumetric segmentation. arXiv preprint 1709.
[62]
Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. science 2006; 313(5786): 504-7.
[63]
Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA, Bottou L. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 2010; 11(12:)
[64]
Nair V, Hinton GE. Rectified linear units improve restricted boltzmann machines. Proceedings of the 27 th International Conference on Machine Learning. Jan 1-3; Haifa, Israel. 2010.
[66]
Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint 1502.
[68]
Sutskever I, Martens J, Hinton GE. Generating text with recurrent neural networks. Proceedings of the 28th International Conference on Machine Learning. 2011 Jan 1-2; Bellevue, WA, USA: Elsevier 2011.
[70]
Szegedy C, Toshev A, Erhan D. Deep neural networks for object detection. Adv Neural Inf Process Syst 2013; 2553-61.
[72]
Taigman Y, Yang M, Ranzato MA, Wolf L. Deepface: Closing the gap to human-level performance in face verification. Proceedings of the IEEE conference on computer vision and pattern recognition. NW Washington, DC. IEEE 2015.
[77]
Everingham M, Winn J. The pascal visual object classes challenge 2012 (voc2012) development kit. Pattern Analysis, Statistical Modelling and Computational Learning, Tech Rep 2012; 25: 8.
[83]
Shin HC, Roberts K, Lu L, Demner-Fushman D, Yao J, Summers RM. Learning to read chest x-rays: Recurrent neural cascade model for automated image annotation. Proceedings of the IEEE conference on computer vision and pattern recognition. June 26-1; Caesars Palace, Las Vegas, Nevada, United States: IEEE 2016.
[85]
Suk HI. Alzheimer’s disease Neuroimaging Initiative.. Deep learning in diagnosis of brain disordersRecent Progress in Brain and Cognitive Engineering. 1st ed.. Dordrecht:Springer Netherlands 2015; pp. 203-13.
[91]
Chen H, Dou Q, Wang X, Qin J, Heng PA. Mitosis detection in breast cancer histology images via deep cascaded networks. 13th AAAI conference on artificial intelligence. 2016 Feb 12-17; Phoenix, Arizon, USA. AAAI 2016.
[99]
Gupta A, Ayhan M, Maida A. Natural image bases to represent neuroimaging data. International conference on machine learning. June 16-21;; Atlanta, USA. ACM 2013.
[101]
Nie D, Wang L, Gao Y, Shen D. Fully convolutional networks for multi-modality isointense infant brain image segmentation. 2016 IEEE 13Th international symposium on biomedical imaging (ISBI).
[102]
Csurka G, Larlus D, Perronnin F, Meylan F. What is a good evaluation measure for semantic segmentation?. BMVC 2013.