[1] LI Jinrang.The study of laryngeal leukoplakia should be further strengthened[J].Chinese Journal of Otorhinolaryngology Head and Neck Surgery,2018,53(8):561-563.(in Chinese)李进让.喉白斑的研究应进一步加强[J].中华耳鼻咽喉头颈外科杂志,2018,53(8):561-563. [2] PUROHIT J P,SHARMA V K,SINGH P N.Leukoplakia:a correlative study of clinical picture and cytohistopathology[J].Indian Journal of Otolaryngology and Head and Neck Surgery,1999,52(1):33-36. [3] GALE N,MICHAELS L,LUZAR B,et al.Current review on squamous intraepithelial lesions of the larynx[J].Histopathology,2009,54(6):639-656. [4] LI Changjiang,ZHANG Na,WANG Shuyi,et al.A new classification of vocal fold leukoplakia by morphological appearance guiding the treatment[J].Acta Oto-Laryngologica,2018,138(6):584-589. [5] CHEN Min,LI Changjiang,YANG Yue,et al.A morphological classification for vocal fold leukoplakia[J].Brazilian Journal of Otorhinolaryngology,2019,85(5):588-596. [6] FANG T J,LIN W N,LEE L Y,et al.Classification of vocal fold leukoplakia by clinical scoring[J].Head and Neck,2016,38(S1):1998-2003. [7] GHAFOORIAN M,KARSSEMEIJER N,HESKES T,et al.Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities[J].Scientific Reports,2017,7(1):5110-5118. [8] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:21-30. [9] RONNEBERGER O,FISCHER P,BROX T.U-Net:convolutional networks for biomedical image segmentation[EB/OL].[2019-09-01].https://arxiv.org/abs/1505.04597. [10] JIANG Hongda,YE Xining.An improved skin disease image segmentation algorithm based on I-Unet network[J].Modern Electronic Technique,2019,42(12):52-56.(in Chinese)蒋宏达,叶西宁.一种改进的I-Unet网络的皮肤病图像分割算法[J].现代电子技术,2019,42(12):52-56. [11] ZHU Hui,QIN Pinle.U-Net pulmonary nodule detection algorithm based on multi-scale feature structure[J].Computer Engineering,2019,45(4):254-261.(in Chinese)朱辉,秦品乐.基于多尺度特征结构的U-Net肺结节检测算法[J].计算机工程,2019,45(4):254-261. [12] LIU Zhe,ZHANG Xiaolin,SONG Yuqing,et al.Liver segmentation with improved U-Net and Morphsnakes algorithm[J].Journal of Image and Graphics,2018,23(8):168-176.(in Chinese)刘哲,张晓林,宋余庆,等.结合改进的U-Net和Morphsnakes的肝脏分割[J].中国图象图形学报,2018,23(8):168-176. [13] CAO Ping,SHENG Qiuyang,PAN Qing,et al.Combining sequence learning and U-Like-Net for hippocampus segmentation[J].Journal of Computer-Aided Design and Computer Graphics,2019,31(8):1382-1390.(in Chinese)曹平,盛邱煬,潘清,等.结合序列学习和U型网络的海马体分割方法[J].计算机辅助设计与图形学学报,2019,31(8):1382-1390. [14] MEHTA R,SIVASWAMY J.M-Net:a convolutional neural network for deep brain structure segmentation[C]//Proceedings of 2017 IEEE International Symposium on Biomedical Imaging Melbourne,Washington D.C.,USA:IEEE Press,2017:12-29. [15] FU Huazhu,CHENG Jun,XU Yanwu,et al.Joint optic disc and cup segmentation based on multi-label deep network and polar transformation[J].IEEE Transactions on Medical Imaging,2018,37(7):1597-1605. [16] ZHOU Z W,SIDDIQUEE M M R,TAJBAKHSH N,et al.U-Net++:a nested U-Net architecture for medical image segmentation[EB/OL].[2019-09-01].https://arxiv.org/abs/1807.10165v1. [17] YU F,WANG D Q,SHELHAMER E,et al.Deep layer aggregation[C]//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:1-7. [18] HUYNH B,DRUKKER K,GIGER M.MO-DE-207B-06:computer-aided diagnosis of breast ultrasound images using transfer learning from deep convolutional neural networks[J].Medical Physics,2016,43(6):3705-3712. [19] LIANG Ming,HU Xiaolin.Recurrent convolutional neural network for object recognition[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:32-40. [20] SZEGEDY C,VANHOUCKE V,IOFFE S,et al.Rethinking the inception architecture for computer vision[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:15-22. [21] MILLETARI F,NAVAB N,AHMADI S A.V-Net:fully convolutional neural networks for volumetric medical image segmentation[C]//Proceedings of 2016 International Conference on 3D Vision.Washington D.C.,USA:IEEE Press,2016:21-30. [22] ZHOU Hao,ZHANG Jun,LEI Jun,et al.Image semantic segmentation based on FCN-CRF model[C]//Proceedings of 2016 International Conference on Image,Vision and Computing.Washington D.C.,USA:IEEE Press,2016:52-60. |