[1] Sridhar D, Krishna I V M. Brain tumor classification using discrete cosine transform and probabilistic neural network［C］∥ International Conference on Signal Processing Image Processing & Pattern Recognition.Andhrapradesh,India:IEEE,2013:92-96.

[2] Wen P Y, Kesari S. Malignant gliomas in adults［J］. New England Journal of Medicine,2008,359(5):492-507.

[3] Bauer S, Wiest R, Nolte L P, et al. A survey of MRI-based medical image analysis for brain tumor studies［J］. Physics in Medicine & Biology,2013,58(13):R97.

[4] Stylli S S, Luwor R B, Ware T M B, et al. Mouse models of glioma［J］. Journal of Clinical Neuroscience,2015,22(4):619-626.

[5] Arevalo-Perez J, Peck K K, Young R J, et al. Dynamic contrast-enhanced perfusion MRI and diffusion-weighted imaging in grading of gliomas［J］. Journal of Neuroimaging,2015,25(5):792-798.

[6] Menze B H, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS) ［J］. IEEE Transactions on Medical Imaging,2015,34(10):1993-2024.

[7] Bakas S, Akbari H, Sotiras A, et al. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features［J］. Nature Scientific Data,2017,4(1):170117.

[8] 刘利雄, 马忠梅, 赵恒博, 等. 一种基于主动轮廓模型的心脏核磁共振图像分割方法［J］. 计算机学报,2012,35(1):146-153.

    Liu Lixiong, Ma Zhongmei, Zhao Hengbo, et al. A method for segmenting cardiac magnetic resonance images using active contours［J］. Chinese Journal Of Computers,2012,35(1):146-153.

[9] Zacharaki E I, Wang S, Chawla S, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme［J］. Magnetic Resonance in Medicine,2010,62(6):1609-1618.

[10] Zulpe N S, Pawar V P. GLCM textural features for Brain Tumor Classification［J］. International Journal of Computer Science Issues,2012,9(3):354.

[11] Khawaldeh S, Pervaiz U, Rafiq A, et al. Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks［J］. Applied Sciences,2017,8(1):27.

[12] Hazlett H C, Gu H, Munsell B C, et al. Early brain development in infants at high risk for autism spectrum disorder［J］. Nature,2017,542(7641):348-351.

[13] Esteva A, Kuprel B, Novoa R A, et al. Dermatologist-level classification of skin cancer with deep neural networks［J］. Nature,2017,542(7639):115.

[14] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks［J］. Science,2006,313(5786):504-507.

[15] Ji S, Xu W, Yang M, et al. 3D convolutional neural networks for human action recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(1):221-231.

[16] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation［J］. Medical Image Computing and Computer-Assisted Intervention,2015,1(1):234-241.

[17] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift［C］∥ International Conference on International Conference on Machine Learning. Lille,France:IEEE,2015:448-456.

[18] Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting［J］. Journal of Machine Learning Research,2014,15(1):1929-1958.

[19] Tustison N J, Avants B B, Cook P A, et al. N4ITK: improved N3 bias correction［J］. IEEE Transactions on Medical Imaging,2010,29(6):1310.

[20] Masko D, Hensman P. The impact of imbalanced training data for convolutional neural networks［J］. Degree Project in Computer Science, KTH Royal Institute of Technology,2015,1(10):82-110.

[21] Bloice M D, Stocker C, Holzinger A. Augmentor: an image augmentation library for machine learning［J］. Journal of Open Source Software,2017,2(19):1708-1713.

[22] Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks［J］. Advances in Neural Information Processing Systems,2012,25(2):1097-1105.

[23] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition［C］∥ International Conference on Learning Representations. Banff,Canada:IEEE,2014:1409-1416.

[24] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition［C］∥ Conference on Computer Vision & Pattern Recognition. Las Vegas,USA:IEEE,2016:770-778.

[25] Cho H, Lee S, Kim J, et al. Classification of the glioma grading using radiomics analysis［J］. PeerJ,2018,6(1):e5982.

[26] Saito T, Rehmsmeier M. Precrec: fast and accurate precision-recall and ROC curve calculations in R［J］. Bioinformatics,2017,33(1):145-147.