[1] HUANG C X, LAI K B, ZHOU L J, et al.Long-term effects of pattern scan laser pan-retinal photocoagulation on diabetic retinopathy in Chinese patients:a retrospective study[J].International Journal of Ophthalmology, 2020, 13(2):239-245. [2] WANG H, YUAN G H, ZHAO X G, et al.Hard exudate detection based on deep model learned information and multi-feature joint representation for diabetic retinopathy screening[J].Computer Methods and Programs in Biomedicine, 2020, 191:1-10. [3] SAMAN G L, GOHAR N, NOOR S, et al.Automatic detection and severity classification of diabetic retinopathy[J].Multimedia Tools and Applications, 2020, 79(43/44):31803-31817. [4] ISHTIAQ U, ABDUL KAREEM S, ABDULLAH E R M F, et al.Diabetic retinopathy detection through artificial intelligent techniques:a review and open issues[J].Multimedia Tools and Applications, 2020, 79(21/22):15209-15252. [5] WU Z, SHI G L, CHEN Y, et al.Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network[J].Artificial Intelligence in Medicine, 2020, 108:1-10. [6] MO J, ZHANG L, FENG Y Q.Exudate-based diabetic macular edema recognition in retinal images using cascaded deep residual networks[J].Neurocomputing, 2018, 290:161-171. [7] 龙胜春, 陈嘉莉, 黄肖肖, 等.基于生成对抗网络的彩色眼底图像硬性渗出检测方法[J].中国生物医学工程学报, 2019, 38(2):157-165. LONG S C, CHEN J L, HUANG X X, et al.hard exudates detection method based on generation adversarial networks in color fundus images[J].Chinese Journal of Biomedical Engineering, 2019, 38(2):157-165.(in Chinese) [8] HUANG C X, ZONG Y S, DING Y M, et al.A new deep learning approach for the retinal hard exudates detection based on superpixel multi-feature extraction and patch-based CNN[J].Neurocomputing, 2021, 452:521-533. [9] PRATHEEBA C, SINGH N N.A novel approach for detection of hard exudates using random forest classifier[J].Journal of Medical Systems, 2019, 43(7):180. [10] KAUR J, MITTAL D.A generalized method for the segmentation of exudates from pathological retinal fundus images[J].Biocybernetics and Biomedical Engineering, 2018, 38(1):27-53. [11] LIM S T, ZAKI W M D W, HUSSAIN A, et al.Automatic classification of diabetic macular edema in digital fundus images[C]//Proceedings of IEEE Colloquium on Humanities, Science and Engineering.Washington D.C., USA:IEEE Press, 2011:265-269. [12] HARANGI B, HAJDU A.Automatic exudate detection by fusing multiple active contours and regionwise classification[J].Computers in Biology and Medicine, 2014, 54:156-171. [13] FRAZ M M, JAHANGIR W, ZAHID S, et al.Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification[J].Biomedical Signal Processing and Control, 2017, 35:50-62. [14] MATEEN M, WEN J H, NASRULLAH N, et al.Exudate detection for diabetic retinopathy using pretrained convolutional neural networks[J].Complexity, 2020, 45:1-10. [15] ZHANG T, QI G J, XIAO B, et al.Interleaved group convolutions[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2017:4383-4392. [16] FU J, LIU J, TIAN H J, et al.Dual attention network for scene segmentation[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2019:3141-3149. [17] RONNEBERGER O, FISCHER P, BROX T.U-Net:convolutional networks for biomedical image segmentation[C]//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany:Springer, 2015:234-241. [18] ABRAHAM N, KHAN N M.A novel focal tversky loss function with improved attention U-Net for lesion segmentation[C]//Proceedings of the 16th International Symposium on Biomedical Imaging.Washington D.C., USA:IEEE Press, 2019:683-687. [19] THEERA-UMPON N, POONKASEM I, AUEPHANWIRIYAKUL S, et al.Hard exudate detection in retinal fundus images using supervised learning[J].Neural Computing and Applications, 2020, 32(17):13079-13096. [20] AL SARIERA T M, RANGARAJAN L, AMARNATH R.Detection and classification of hard exudates in retinal images[J].Journal of Intelligent & Fuzzy Systems, 2020, 38(2):1943-1949. [21] LONG S C, HUANG X X, CHEN Z Q, et al.Automatic detection of hard exudates in color retinal images using dynamic threshold and SVM classification:algorithm development and evaluation[J].BioMed Research International, 2019, 30:1-13. [22] WORAPAN K, WU Q, RIETHIPRAVAT P, et al.Hard exudates segmentation based on learned initial seeds and iterative graph cut[J].Computer Methods and Programs in Biomedicine, 2018, 158:173-183. [23] YU S, XIAO D, KANAGASINGAM Y.Exudate detection for diabetic retinopathy with convolutional neural networks[C]//Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.Washington D.C., USA:IEEE Press, 2017:1744-1747. [24] LIU Q, ZOU B J, CHEN J, et al.A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images[J].Computerized Medical Imaging and Graphics, 2017, 55:78-86. [25] GUO S, LI T, KANG H, et al.L-Seg:an end-to-end unified framework for multi-lesion segmentation of fundus images[J].Neurocomputing, 2019, 349:52-63. |