[1] [1] WU Guo-qing, LI Ze-ju, WANG Yuan-yuan, et al. Primary central nervous system lymphoma and glioblastoma image differentiation based on sparse representation system［J］. Journal of Biomedical Engineering, 2018, 35(5): 754-760.

[2] [2] XIONG Jiao-jiao, LU Hong-yang, ZHANG Ming-hui, et al. Convolutional sparse coding in gradient domain for MRI reconstruction［J］. Acta Automatica Sinica, 2017, 43(10): 1841-1849.

[3] [3] ZHANG Qiong-min, ZHANG Jing, WANG Min-tang, et al. Head and neck tumor segmentation based on augmented gradient level set method［J］. Journal of Biomedical Engineering, 2015, 32(4): 887-891+904.

[4] [4] ZHANG Teng-da, LV Xiao-qi, REN Xiao-yin, et al. Brain tumor MR image segmentation method based on fuzzy level set［J］. Modern Electronics Technique, 2016, 39(18): 91-95.

[5] [5] LI Na, XIONG Zhi-yong, XIE Jin, et al. Brain tumor segmentation on multi-modality magnetic resonance images based on tamura texture feature and SVM model［J］. Journal of South-Central University for Nationalities (Natural Science Edition), 2018, 37(3): 144-149.

[6] [6] REN S, HE K, GIRSHICK R,et al. Faster R-CNN: towards real-time object detection with region proposal networks ［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149.

[7] [7] YUAN Wen-hao, SUN Wen-zhu, XIA Bin, et al. Improving speech enhancement in unseen noise using deep convolutional neural network［J］. Acta Automatica Sinica, 2018, 44(4): 751-759.

[8] [8] XIAO Wen, YANG Lu, PAN Feng, et al. Automatic phase aberration compensation for digital holographic microscopy combined with phase fitting and deep learning［J］. Acta Photonica Sinica, 2018, 47(12): 1210001.

[9] [9] MA Hao-yu, XU Zhi-Hai, FENG Hua-jun, et al. Image super-resolution based on tiny recurrent convolutional neural network［J］. Acta Photonica Sinica, 2018, 47(4): 0410004.

[11] [11] CHEN K, DING G,HAN J. Attribute-based supervised deep learning model for action recognition［J］. Frontiers of Computer Science, 2017, 11(2):219-229.

[12] [12] SHAN H, ZHANG Y, YANG Q, et al. 3-D convolutional encoder-decoder network for low-dose CT via transfer learning from a 2-D trained network［J］. IEEE Transactions on Medical Imaging, 2018, 37(6): 1522.

[13] [13] PEREIRA M B, WALLROTH M, JONSSON V, et al. Comparison of normalization methods for the analysis of metagenomic gene abundance data［J］. Bmc Genomics, 2018, 19(1): 274.

[14] [14] HAN X, DAI Q. Batch-normalized Mlpconv-wise supervised pre-training network in network［J］. Applied Intelligence, 2017, 48(7): 142-155.

[15] [15] BORGHAMMER P, JONSDOTTIR K Y, CUMMING P, et al. Normalization in PET group comparison studies-the importance of a valid reference region［J］. Neuroimage, 2008, 40(2): 529-540.

[16] [16] LAGER C J, ESFANDIARI N H, SUBAUSTE A R, et al. Milestone weight loss goals (weight normalization and remission of obesity) after gastric bypass surgery: long-term results from the university of Michigan［J］. Obesity Surgery, 2017, 27(7): 1659-1666.

[17] [17] ELAD M, MILANFAR P. Style transfer via texture synthesis［J］.IEEE Transactions on Image Processing, 2017, 26(5): 2338-2351.

[18] [18] JIANG N, WANG L. Quantum image scaling using nearest neighbor interpolation［J］.Quantum Information Processing, 2015, 14(5): 1559-1571.