第2期 韩璐，等：多尺度特征融合网络的视网膜OCT图像分类 ·367· America A,Optics,image science,and vision,2016, [23]朱纳，李明.多层次可选择核卷积用于视网膜图像分 33(4):455-463 类[EB/OL].(2021-10-11)[2021-12-01].htps:/ns-cnki- [16]CIULLA T A.AMADOR A G.ZINMAN B.Diabetic net.wvpn.hrbeu.edu.cn/kcms/detail/50.1181.N.20211009. retinopathy and diabetic macular edema:patho- 1903.006.html. physiology,screening,and novel therapies[J].Diabetes ZHU Na,LI Ming.Multi-level selective kernel convolu- care,2003,26(9):2653-2664. tion for retinal image classification[EB/OL].(2021-10- [17刀张添福，钟舜聪，连超铭，等.基于深度学习特征融合 11)[2021-12-01].https://kns-cnki-net.wvpn.hrbeu.edu. 的视网膜图像分类[J].激光与光电子学进展， cn/kcms/detail/50.1181.N.20211009.1903.006.html. 2020(24)258-265. [24]BHADRA R,KAR S.Retinal disease classification from ZHANG Tianfu,ZHONG Shuncong,LIAN Chaoming, optical coherence tomographical scans using mul- et al.Deep learning feature fusion-based retina image tilayered convolution neural network[Cl//2020 IEEE Ap- classification[J].Laser optoelectronics progress, plied Signal Processing Conference.Kolkata,India. 2020(24):258-265. IEEE,2020:212-216. [18]SIMONYAN K,ZISSERMAN A.Very deep convolu- [25]于海琛.基于SE-Block的视网膜疾病分类方法研究 tional networks for large-scale image recognition [D1.长春：吉林大学，2019 [EB/OL].(2014-09-04)[2021-01-01].https:/arxiv YU Haichen.Research on classification of retinal dis- org/abs/1409.1556 eases based on SE-block[D].Changchun:Jilin Uni- [19]HU Jie,SHEN Li,SUN Gang.Squeeze-and-excitation versity,2019. networks[Cl//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City,USA. 作者简介： IEEE.2018:7132-7141. 韩璐，硕土，主要研究方向为图像 [20]WOO S,PARK J,LEE J Y,et al.CBAM:convolutional 识别、深度学习。 block attention module[M]//Computer Vision-ECCV 2018.Cham:Springer International Publishing,2018: 3-19 [21]WANG Qilong,WU Banggu,ZHU Pengfei,et al.ECA- net:efficient channel attention for deep convolutional neural networks[C]//2020 IEEE/CVF Conference on 毕晓君，教授，博士生导师，主要 Computer Vision and Pattern Recognition.Seattle,USA. 研究方向为信息智能处理、数字图像 处理、智能优化算法及机器学习。主 IEEE,2020:11531-11539 持国家自然科学基金面上项目2项 [22]CHEN L C,PAPANDREOU G,KOKKINOS I,et al 科技部国际合作项目面上项目1项 DeepLab:semantic image segmentation with deep con- 教育部博士点基金项目1项、工业和 volutional nets,atrous convolution,and fully connected 信息化部海洋工程装备科研项目子项 CRFs[J].IEEE transactions on pattern analysis and ma- 目1项、民品横向课题1项，获国家专利8项。发表学术论 chine intelligence,2018,40(4):834-848. 文170余篇，出版学术专著3部。America A, Optics, image science, and vision, 2016, 33(4): 455–463. CIULLA T A, AMADOR A G, ZINMAN B. Diabetic retinopathy and diabetic macular edema: patho￾physiology, screening, and novel therapies[J]. Diabetes care, 2003, 26(9): 2653–2664. [16] 张添福, 钟舜聪, 连超铭, 等. 基于深度学习特征融合 的视网膜图像分 类 [J]. 激光与光电子学进展 , 2020(24): 258–265. ZHANG Tianfu, ZHONG Shuncong, LIAN Chaoming, et al. Deep learning feature fusion-based retina image classification[J]. Laser & optoelectronics progress, 2020(24): 258–265. [17] SIMONYAN K, ZISSERMAN A. Very deep convolu￾tional networks for large-scale image recognition [EB/OL]. (2014-09-04)[2021-01-01].https://arxiv. org/abs/1409.1556. [18] HU Jie, SHEN Li, SUN Gang. Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA. IEEE, 2018: 7132−7141. [19] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[M]//Computer Vision-ECCV 2018. Cham: Springer International Publishing, 2018: 3−19. [20] WANG Qilong, WU Banggu, ZHU Pengfei, et al. ECA￾net: efficient channel attention for deep convolutional neural networks[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA. IEEE, 2020: 11531−11539. [21] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: semantic image segmentation with deep con￾volutional nets, atrous convolution, and fully connected CRFs[J]. IEEE transactions on pattern analysis and ma￾chine intelligence, 2018, 40(4): 834–848. [22] 朱纳, 李明. 多层次可选择核卷积用于视网膜图像分 类 [EB/OL]. (2021-10-11)[2021-12-01].https://kns-cnki￾net.wvpn.hrbeu.edu.cn/kcms/detail/50.1181.N.20211009. 1903.006.html. ZHU Na, LI Ming. Multi-level selective kernel convolu￾tion for retinal image classification[EB/OL]. (2021-10- 11)[2021-12-01].https://kns-cnki-net.wvpn.hrbeu.edu. cn/kcms/detail/50.1181.N.20211009.1903.006.html.. [23] BHADRA R, KAR S. Retinal disease classification from optical coherence tomographical scans using mul￾tilayered convolution neural network[C]//2020 IEEE Ap￾plied Signal Processing Conference . Kolkata, India. IEEE, 2020: 212-216. [24] 于海琛. 基于 SE-Block 的视网膜疾病分类方法研究 [D]. 长春: 吉林大学, 2019. YU Haichen. Research on classification of retinal dis￾eases based on SE-block[D]. Changchun: Jilin Uni￾versity, 2019. [25] 作者简介： 韩璐，硕士，主要研究方向为图像 识别、深度学习。 毕晓君，教授，博士生导师，主要 研究方向为信息智能处理、数字图像 处理、智能优化算法及机器学习。主 持国家自然科学基金面上项目 2 项、 科技部国际合作项目面上项目 1 项、 教育部博士点基金项目 1 项、工业和 信息化部海洋工程装备科研项目子项 目 1 项、民品横向课题 1 项，获国家专利 8 项。发表学术论 文 170 余篇，出版学术专著 3 部。 第 2 期 韩璐，等：多尺度特征融合网络的视网膜 OCT 图像分类 ·367·