Xiao-Qing Zhang, Yan Hu, Zun-Jie Xiao, Jian-Sheng Fang, Risa Higashita, Jiang Liu. Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities: A Survey. Machine Intelligence Research, vol. 19, no. 3, pp.184-208, 2022. https://doi.org/10.1007/s11633-022-1329-0
Citation: Xiao-Qing Zhang, Yan Hu, Zun-Jie Xiao, Jian-Sheng Fang, Risa Higashita, Jiang Liu. Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities: A Survey. Machine Intelligence Research, vol. 19, no. 3, pp.184-208, 2022. https://doi.org/10.1007/s11633-022-1329-0

Machine Learning for Cataract Classification/Grading on Ophthalmic Imaging Modalities: A Survey

doi: 10.1007/s11633-022-1329-0
More Information
  • Author Bio:

    Xiao-Qing Zhang received the B. Sc. degree in water conservancy and hydropower engineering from South China Agricultural University, China in 2016, the M. Sc. degree in computer engineering from Zhengzhou University. Currently, he is a Ph. D. degree candidate with Department of Computer Science and Engineering, Southern University of Science and Technology, China. His research interests include deep learning, interpretability, and medical image processing. E-mail: 11930927@mail.sustech.edu.cnORCID iD: 0000-0003-1518-4781

    Yan Hu received the B. Sc. and M. Sc. degrees in computer science from Northeast Normal University, China in 2008 and 2011, respectively, and the Ph. D. degree in optics from University of Tokyo, Japan in 2016. Currently, she is a research assistant professor in Department of Computer Science and Engineering, Southern University of Science and Technology, China. Her research interests include surgical assistance, intraoperative navigation, and medical image processing. E-mail: huy3@sustech.edu.cn

    Zun-Jie Xiao received the B. Sc. degree in bioinformatics from Dalian University of Technology, China in 2019. Currently, he is a Ph. D. degree candidate with Department of Computer Science and Engineering, Southern University of Science and Technology, China. His research interests include deep learning and medical image processing. E-mail: 11930387@mail.sustech.edu.cn

    Jian-Sheng Fang received the B. Sc. degree in computer science and technology from Harbin Institute of Technology, China, the M. Sc. degree in computer application from Sun Yat-sen University, China. He is a Ph. D. degree candidate with Department of Computer Science and Engineering, Southern University of Science and Technology, China. His research interests include deep learning and image retrieval. E-mail: 11949039@mail.sustech.edu.cn

    Risa Higashita received the Ph. D. degree in biomedical engineering from Nagoya University, Japan in 2004. Currently, she is a visiting professor in Department of Computer Science and Engineering, Southern University of Science and Technology, China. Her research interests include medical image processing and ophthalmology. E-mail: lisahigashita@gmail.com

    Jiang Liu received the B. Sc. degree in computer science from University of Science and Technology of China, China in 1988, the M. Sc. and Ph. D. degrees from National University of Singapore, Singapore in 1992 and 2004, respectively. He founded the Intelligent Medical Imaging Research Team which was once the world′s largest ophthalmic medical image processing team, focusing on ophthalmic artificial intelligence research. Currently, he is a professor in Department of Computer Science and Engineering, Southern University of Science and Technology, China. His research interests include artificial intelligence, eye-brain research, precision medicine, and surgical robots. E-mail: liuj@sustech.edu.cn (Corresponding author)ORCID iD: 0000-0001-5281-6505

  • Received Date: 2022-01-16
  • Accepted Date: 2022-03-28
  • Publish Date: 2022-06-01
  • Cataracts are the leading cause of visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading, aiming to prevent cataracts early and improve clinicians′ diagnosis efficiency. This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning methods and deep learning methods. This survey also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research.

     

  • †These authors contributed equally to this work.
  • loading
  • [1]
    R. R. A. Bourne, S. R. Flaxman, T. Braithwaite, M. V. Cicinelli, A. Das, J. B. Jonas, J. Keeffe, J. H. Kempen, J. Leasher, H. Limburg, K. Naidoo, K. Pesudovs, S. Resnikoff, A. Silvester, G. A. Stevens, N. Tahhan, T. Y. Wong, H. R. Taylor. Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: A systematic review and meta-analysis. The Lancet Global Health, vol. 5, no. 9, pp. e888–e897, 2017. DOI: 10.1016/S2214-109X(17)30293-0.
    [2]
    D. Pascolini, S. P. Mariotti. Global estimates of visual impairment: 2010. British Journal of Ophthalmology, vol. 96, no. 5, pp. 614–618, 2012. DOI: 10.1136/bjophthalmol-2011-300539.
    [3]
    P. A. Asbell, I. Dualan, J. Mindel, D. Brocks, M. Ahmad, S. Epstein. Age-related cataract. The Lancet, vol. 365, no. 9459, pp. 599–609, 2005. DOI: 10.1016/S0140-6736(05)17911-2.
    [4]
    Y. C. Liu, M. Wilkins, T. Kim, B. Malyugin, J. S. Mehta. Cataracts. The Lancet, vol. 390, no. 10094, pp. 600–612, 2017. DOI: 10.1016/S0140-6736(17)30544-5.
    [5]
    H. Q. Li, J. H. Lim, J. Liu, D. W. K. Wong, N. M. Tan, S. J. Lu, Z. Zhang, T. Y. Wong. An automatic diagnosis system of nuclear cataract using slit-lamp images. In Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Minneapolis, USA, pp. 3693–3696, 2009. DOI: 10.1109/IEMBS.2009.5334735.
    [6]
    H. Q. Li, J. H. Lim, J. Liu, D. W. K. Wong, Y. Foo, Y. Sun, T. Y. Wong. Automatic detection of posterior subcapsular cataract opacity for cataract screening. In Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology, IEEE, Buenos Aires, Argentina, pp. 5359–5362, 2010. DOI: 10.1109/IEMBS.2010.5626467.
    [7]
    M. Chew, P. P. C. Chiang, Y. F. Zheng, R. Lavanya, R. Y. Wu, S. M. Saw, T. Y. Wong, E. L. Lamoureux. The impact of cataract, cataract types, and cataract grades on vision-specific functioning using rasch analysis. American journal of Ophthalmology, vol. 154, no. 1, pp. 29–38.e2, 2012. DOI: 10.1016/j.ajo.2012.01.033.
    [8]
    E. P. Long, H. T. Lin, Z. Z. Liu, X. H. Wu, L. M. Wang, J. W. Jiang, Y. Y. An, Z. L. Lin, X. Y. Li, J. J. Chen, J. Li, Q. Z. Cao, D. N. Wang, X. Y. Liu, W. R. Chen, Y. Z. Liu. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nature Biomedical Engineering, vol. 1, no. 2, Article number 0024, 2017. DOI: 10.1038/s41551-016-0024.
    [9]
    W. Huang, H. Q. Li, K. L. Chan, J. H. Lim, J. Liu, T. Y. Wong. A computer-aided diagnosis system of nuclear cataract via ranking. In Proceedings of the 12th International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, London, UK, pp. 803–810, 2009. DOI: 10.1007/978-3-642-04271-3_97.
    [10]
    Y. W. Xu, L. X. Duan, D. W. K. Wong, T. Y. Wong, J. Liu. Semantic reconstruction-based nuclear cataract grading from slit-lamp lens images. In Proceedings of the 19th International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, Athens, Greece, pp. 458–466, 2016. DOI: 10.1007/978-3-319-46726-9_53.
    [11]
    Y. W. Xu, X. T. Gao, S. Lin, D. W. K. Wong, J. Liu, D. Xu, C. Y. Cheng, C. Y. Cheung, T. Y. Wong. Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression. In Proceedings of the 16th International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, Nagoya, Japan, pp. 468–475, 2013. DOI: 10.1007/978-3-642-40763-5_58.
    [12]
    X. T. Gao, H. Q. Li, J. H. Lim, T. Y. Wong. Computer-aided cataract detection using enhanced texture features on retro-illumination lens images. In Proceedings of the 18th IEEE International Conference on Image Processing, IEEE, Brussels, Belgium, pp. 1565–1568, 2011. DOI: 10.1109/ICIP.2011.6115746.
    [13]
    R. Srivastava, X. T. Gao, F. S. Yin, D. W. Wong, J. Liu, C. Y. Cheung, T. Y. Wong. Automatic nuclear cataract grading using image gradients. Journal of Medical Imaging, vol. 1, no. 1, Article number 014502, 2014. DOI: 10.1117/1.JMI.1.1.014502.
    [14]
    A. U. Patwari, M. D. Arif, N. A. Chowdhury, A. Arefin, I. Imam. Detection, categorization, and assessment of eye cataracts using digital image processing. In Proceedings of the 1st International Conference on Interdisciplinary Research and Development, Suomi, Thailand, pp. 22.1–22.5, 2011.
    [15]
    Y. N. Fuadah, A. W. Setiawan, T. L. R. Mengko. Performing high accuracy of the system for cataract detection using statistical texture analysis and K-nearest neighbor. In Proceedings of International Seminar on Intelligent Technology and its Applications, IEEE, Surabaya, Indonesia, pp. 85–88, 2015. DOI: 10.1109/ISITIA.2015.7219958.
    [16]
    S. Pathak, B. Kumar. A robust automated cataract detection algorithm using diagnostic opinion based parameter thresholding for telemedicine application. Electronics, vol. 5, no. 3, Article number 57, 2016. DOI: 10.3390/electronics5030057.
    [17]
    A. A. Khan, M. U. Akram, A. Tariq, F. Tahir, K. Wazir. Automated computer aided detection of cataract. In Proceedings of the 3rd International Afro-European Conference for Industrial Advancement , Mrrakesh, Morrocco, pp. 340–349, 2018. DOI: 10.1007/978-3-319-60834-1_34.
    [18]
    J. J. Yang, J. Q. Li, R. F. Shen, Y. Zeng, J. He, J. Bi, Y. Li, Q. Y. Zhang, L. H. Peng, Q. Wang. Exploiting ensemble learning for automatic cataract detection and grading. Computer Methods and Programs in Biomedicine, vol. 124, pp. 45–57, 2016. DOI: 10.1016/j.cmpb.2015.10.007.
    [19]
    Z. Q. Qiao, Q. Y. Zhang, Y. Y. Dong, J. J. Yang. Application of SVM based on genetic algorithm in classification of cataract fundus images. In Proceedings of IEEE International Conference on Imaging Systems and Techniques, IEEE, Beijing, China, 2017. DOI: 10.1109/IST.2017.8261541.
    [20]
    X. Q. Zhang, Z. J. Xiao, R. Higashita, W. Chen, Y. Hu, J. Yuan, J. Liu. Nuclear cataract classification based on multi-region fusion attention network model. Journal of Image and Graphics, vol. 27, no. 3, pp. 948–960, 2022. DOI: 10.11834/jig.210735. (in Chinese)
    [21]
    Z. Zhang, R. Srivastava, H. Y. Liu, X. Y. Chen, L. X. Duan, D. W. K. Wong, C. K. Kwoh, T. Y. Wong, J. Liu. A survey on computer aided diagnosis for ocular diseases. BMC Medical Informatics and Decision Making, vol. 14, no. 1, Article number 80, 2014. DOI: 10.1186/1472-6947-14-80.
    [22]
    I. Shaheen, A. Tariq. Survey analysis of automatic detection and grading of cataract using different imaging modalities. Applications of Intelligent Technologies in Healthcare, F. Khan, M. A. Jan, M. Alam, Eds., Cham, Germany: Springer, pp. 35–45, 2019. DOI: 10.1007/978-3-319-96139-2_4.
    [23]
    H. I. M. Lopez, J. C. S. Garcia, J. A. D. Mendez. Cataract detection techniques: A review. IEEE Latin America Transactions, vol. 14, no. 7, pp. 3074–3079, 2016. DOI: 10.1109/TLA.2016.7587604.
    [24]
    R. Zafar, M. Sharif, M. Yasmin. A survey on the prevalence of cataract and its accompanying risk factors. Current Medical Imaging, vol. 14, no. 2, pp. 251–262, 2018. DOI: 10.2174/1573405613666170331103423.
    [25]
    H. E. Gali, R. Sella, N. A. Afshari. Cataract grading systems: A review of past and present. Current Opinion in Ophthalmology, vol. 30, no. 1, pp. 13–18, 2019. DOI: 10.1097/ICU.0000000000000542.
    [26]
    J. H. L. Goh, Z. W. Lim, X. L. Fang, A. Anees, S. Nusinovici, T. H. Rim, C. Y. Cheng, Y. C. Tham. Artificial intelligence for cataract detection and management. Asia-Pacific Journal of Ophthalmology, vol. 9, no. 2, pp. 88–95, 2020. DOI: 10.1097/01.APO.0000656988.16221.04.
    [27]
    A. F. Fercher, H. C. Li, C. K. Hitzenberger. Slit lamp laser Doppler interferometer. Lasers in Surgery and Medicine, vol. 13, no. 4, pp. 447–452, 1993. DOI: 10.1002/lsm.1900130409.
    [28]
    S. R. Waltman, H. E. Kaufman. A new objective slit lamp fluorophotometer. Investigative Ophthalmology, vol. 9, no. 4, pp. 247–249, 1970.
    [29]
    M. A. Vivino, A. Mahurkar, B. Trus, M. L. Lopez, M. Datiles. Quantitative analysis of retroillumination images. Eye, vol. 9, no. 1, pp. 77–84, 1995. DOI: 10.1038/eye.1995.12.
    [30]
    A. Gershenzon, L. D. Robman. New software for lens retro-illumination digital image analysis. Australian and New Zealand Journal of Ophthalmology, vol. 27, no. 3–4, pp. 170–172, 1999. DOI: 10.1046/j.1440-1606.1999.00201.x.
    [31]
    C. C. Huang, R. M. Chen, P. H. Tsui, Q. F. Zhou, M. S. Humayun, K. K. Shung. Measurements of attenuation coefficient for evaluating the hardness of a cataract lens by a high-frequency ultrasonic needle transducer. Physics in Medicine &Biology, vol. 54, no. 19, pp. 5981–5994, 2009. DOI: 10.1088/0031-9155/54/19/021.
    [32]
    C. C. Huang, H. Ameri, C. DeBoer, A. P. Rowley, X. C. Xu, L. Sun, S. H. Wang, M. S. Humayun, K. K. Shung. Evaluation of lens hardness in cataract surgery using high-frequency ultrasonic parameters in vitro. Ultrasound in Medicine &Biology, vol. 33, no. 10, pp. 1609–1616, 2007. DOI: 10.1016/j.ultrasmedbio.2007.05.002.
    [33]
    P. H. Tsui, C. C. Chang. Imaging local scatterer concentrations by the Nakagami statistical model. Ultrasound in Medicine &Biology, vol. 33, no. 4, pp. 608–619, 2007. DOI: 10.1016/j.ultrasmedbio.2006.10.005.
    [34]
    P. H. Tsui, C. K. Yeh, Y. Y. Liao, C. C. Chang, W. H. Kuo, K. J. Chang, C. N. Chen. Ultrasonic Nakagami imaging: A strategy to visualize the scatterer properties of benign and malignant breast tumors. Ultrasound in Medicine &Biology, vol. 36, no. 2, pp. 209–217, 2010. DOI: 10.1016/j.ultrasmedbio.2009.10.006.
    [35]
    P. H. Tsui, C. C. Huang, C. C. Chang, S. H. Wang, K. K. Shung. Feasibility study of using high-frequency ultrasonic Nakagami imaging for characterizing the cataract lens in vitro. Physics in Medicine &Biology, vol. 52, no. 21, pp. 6413–6425, 2007. DOI: 10.1088/0031-9155/52/21/005.
    [36]
    P. H. Tsui, M. C. Ho, D. I. Tai, Y. H. Lin, C. Y. Wang, H. Y. Ma. Acoustic structure quantification by using ultrasound Nakagami imaging for assessing liver fibrosis. Scientific Reports, vol. 6, Article number 33075, 2016. DOI: 10.1038/srep33075.
    [37]
    A. Plesch, U. Klingbeil, J. Bille. Digital laser scanning fundus camera. Applied Optics, vol. 26, no. 8, pp. 1480–1486, 1987. DOI: 10.1364/AO.26.001480.
    [38]
    O. Pomerantzeff, R. H. Webb, F. C. Delori. Image formation in fundus cameras. Investigative Ophthalmology &Visual Science, vol. 18, no. 6, pp. 630–637, 1979.
    [39]
    L. C. Cao, H. Q. Li, Y. J. Zhang, L. Zhang, L. Xu. Hierarchical method for cataract grading based on retinal images using improved Haar wavelet. Information Fusion, vol. 53, pp. 196–208, 2020. DOI: 10.1016/j.inffus.2019.06.022.
    [40]
    M. Ang, M. Baskaran, R. M. Werkmeister, J. Chua, D. Schmidl, V. A. dos Santos, G. Garhöfer, J. S. Mehta, L. Schmetterer. Anterior segment optical coherence tomography. Progress in Retinal and Eye Research, vol. 66, pp. 132–156, 2018. DOI: 10.1016/j.preteyeres.2018.04.002.
    [41]
    R. M. Werkmeister, S. Sapeta, D. Schmidl, G. Garhöfer, G. Schmidinger, V. A. dos Santos, G. C. Aschinger, I. Baumgartner, N. Pircher, F. Schwarzhans, A. Pantalon, H. Dua, L. Schmetterer. Ultrahigh-resolution OCT imaging of the human cornea. Biomedical Optics Express, vol. 8, no. 2, pp. 1221–1239, 2017. DOI: 10.1364/BOE.8.001221.
    [42]
    N. Hirnschall, S. Amir-Asgari, S. Maedel, O. Findl. Predicting the postoperative intraocular lens position using continuous intraoperative optical coherence tomography measurements. Investigative Ophthalmology &Visual Science, vol. 54, no. 8, pp. 5196–5203, 2013. DOI: 10.1167/iovs.13-11991.
    [43]
    N. Yamazaki, A. Kobayashi, H. Yokogawa, Y. Ishibashi, Y. Oikawa, M. Tokoro, K. Sugiyama. In vivo imaging of radial keratoneuritis in patients with acanthamoeba keratitis by anterior-segment optical coherence tomography. Ophthalmology, vol. 121, no. 11, pp. 2153–2158, 2014. DOI: 10.1016/j.ophtha.2014.04.043.
    [44]
    N. Hirnschall, T. Buehren, F. Bajramovic, M. Trost, T. Teuber, O. Findl. Prediction of postoperative intraocular lens tilt using swept-source optical coherence tomography. Journal of Cataract &Refractive Surgery, vol. 43, no. 6, pp. 732–736, 2017. DOI: 10.1016/j.jcrs.2017.01.026.
    [45]
    I. Grulkowski, S. Manzanera, L. Cwiklinski, J. Mompeán, A. de Castro, J. M. Marin, P. Artal. Volumetric macro- and micro-scale assessment of crystalline lens opacities in cataract patients using long-depth-range swept source optical coherence tomography. Biomedical Optics Express, vol. 9, no. 8, pp. 3821–3833, 2018. DOI: 10.1364/BOE.9.003821.
    [46]
    D. Pawliczek, C. Dalke, H. Fuchs, V. Gailus-Durner, M. H. de Angelis, J. Graw, O. V. Amarie. Spectral domain-optical coherence tomography (SD-OCT) as a monitoring tool for alterations in mouse lenses. Experimental Eye Research, vol. 190, Article number 107871, 2020. DOI: 10.1016/j.exer.2019.107871.
    [47]
    L. T. Chylack Jr, J. K. Wolfe, D. M. Singer, M. C. Leske, M. A. Bullimore, I. L. Bailey, J. Friend, D. McCarthy, S. Y. Wu. The lens opacities classification system III. Archives of Ophthalmology, vol. 111, no. 6, pp. 831–836, 1993. DOI: 10.1001/archopht.1993.01090060119035.
    [48]
    L. T. Chylack Jr, M. C. Leske, R. Sperduto, P. Khu, D. McCarthy. Lens opacities classification system. Archives of Ophthalmology, vol. 106, no. 3, pp. 330–334, 1988. DOI: 10.1001/archopht.1988.01060130356020.
    [49]
    L. T. Chylack, M. C. Leske, D. McCarthy, P. Khu, T. Kashiwagi, R. Sperduto. Lens opacities classification system II (LOCS II). Archives of Ophthalmology, vol. 107, no. 7, pp. 991–997, 1989. DOI: 10.1001/archopht.1989.01070020053028.
    [50]
    B. E. K. Klein, R. Klein, K. L. P. Linton, Y. L. Magli, M. W. Neider. Assessment of cataracts from photographs in the beaver dam eye study. Ophthalmology, vol. 97, no. 11, pp. 1428–1433, 1990. DOI: 10.1016/S0161-6420(90)32391-6.
    [51]
    The Age-Related Eye Disease Study Research Group. The age-related eye disease study (AREDS) system for classifying cataracts from photographs: AREDS Report No. 4. American Journal of Ophthalmology, vol. 131, no. 2, pp. 167–175, 2001. DOI: 10.1016/S0002-9394(00)00732-7.
    [52]
    S. H. Fan, C. R. Dyer, L. Hubbard, B. Klein. An automatic system for classification of nuclear sclerosis from slit-lamp photographs. In Proceedings of the 6th International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, Montreal, Canada, pp. 592-601, 2003. DOI: 10.1007/978-3-540-39899-8_73.
    [53]
    J. M. Sparrow, A. J. Bron, N. A. P. Brown, W. Ayliffe, A. R. Hill. The oxford clinical cataract classification and grading system. International Ophthalmology, vol. 9, no. 4, pp. 207–225, 1986. DOI: 10.1007/BF00137534.
    [54]
    A. B. Hall, J. R. Thompson, J. S. Deane, A. R. Rosenthal. LOCS III versus the oxford clinical cataract classification and grading system for the assessment of nuclear, cortical and posterior subcapsular cataract. Ophthalmic Epidemiology, vol. 4, no. 4, pp. 179–194, 1997. DOI: 10.3109/09286589709059192.
    [55]
    S. K. West, F. Rosenthal, H. S. Newland, H. R. Taylor. Use of photographic techniques to grade nuclear cataracts. Investigative Ophthalmology &Visual Science, vol. 29, no. 1, pp. 73–77, 1988.
    [56]
    B. Thylefors, L. T. Chylack Jr, K. Konyama, K. Sasaki, R. Sperduto, H. R. Taylor, S. West. A simplified cataract grading system the WHO Cataract Grading Group. Ophthalmic Epidemiology, vol. 9, no. 2, pp. 83–95, 2002. DOI: 10.1076/opep.9.2.83.1523.
    [57]
    WHO Programme for the Prevention of Blindness, WHO Cataract Grading Group. A Simplified Cataract Grading System, WHO/PBL/01.81. World Health Organization, Switzerland, 2001.
    [58]
    L. Xu, C. W. Yang, H. Yang, S. Wang, Y. Y. Shi, X. D. Song. The study of predicting the visual acuity after phacoemulsification according to the blur level of fundus photography. Ophthalmology in China, vol. 19, no. 2, pp. 81–83, 2010. (in Chinese)
    [59]
    J. Liu, D. W. K. Wong, Z. Zhang, B. H. Lee, X. T. Gao, F. S. Yin, J. L. Zhang, M. T. Htoo. Integrating research, clinical practice and translation: The Singapore experience. In Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Osaka, Japan, pp. 7148–7151, 2013. DOI: 10.1109/EMBC.2013.6611206.
    [60]
    J. W. Jiang, S. T. Lei, M. M. Zhu, R. Y. Li, J. Y. Yue, J. J. Chen, Z. W. Li, J. M. Gong, D. R. Lin, X. H. Wu, Z. L. Lin, H. T. Lin. Improving the generalizability of infantile cataracts detection via deep learning-based lens partition strategy and multicenter datasets. Frontiers in Medicine, vol. 8, Article number 470, 2021. DOI: 10.3389/FMED.2021.664023.
    [61]
    J. Cuadros, G. Bresnick. EyePACS: An adaptable telemedicine system for diabetic retinopathy screening. Journal of Diabetes Science and Technology, vol. 3, no. 3, pp. 509–516, 2009. DOI: 10.1177/193229680900300315.
    [62]
    T. Pratap, P. Kokil. Computer-aided diagnosis of cataract using deep transfer learning. Biomedical Signal Processing and Control, vol. 53, Article number 101533, 2019. DOI: 10.1016/j.bspc.2019.04.010.
    [63]
    A. D. Hoover, V. Kouznetsova, M. Goldbaum. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging, vol. 19, no. 3, pp. 203–210, 2000. DOI: 10.1109/42.845178.
    [64]
    T. Kauppi, V. Kalesnykiene, J. K. Kamarainen, L. Lensu, I. Sorri, H. Uusitalo, H. Kälviäinen, J. Pietilä. DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy Algorithms. Lappeenranta University of Technology, Finland, 2006. [Online], Available: https://www.it.lut.fi/project/imageret/diaretdb0/.
    [65]
    E. Decenciére, G. Cazuguel, X. Zhang, G. Thibault, J. C. Klein, F. Meyer, B. Marcotegui, G. Quellec, M. Lamard, R. Danno, D. Elie, P. Massin, Z. Viktor, A. Erginay, B. Laÿ, A. Chabouis. Teleophta: Machine learning and image processing methods for teleophthalmology. IRBM, vol. 34, no. 2, pp. 196–203, 2013. DOI: 10.1016/j.irbm.2013.01.010.
    [66]
    E. Decenciére, X. W. Zhang, G. Cazuguel, B. Lay, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, B. Charton, J. C. Klein. Feedback on a publicly distributed image database: The messidor database. Image Analysis &Stereology, vol. 33, no. 3, pp. 231–234, 2014. DOI: 10.5566/ias.1155.
    [67]
    J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, B. Van Ginneken. Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging, vol. 23, no. 4, pp. 501–509, 2004. DOI: 10.1109/TMI.2004.825627.
    [68]
    C. Hernandez-Matas, X. Zabulis, A. Triantafyllou, P. Anyfanti, S. Douma, A. A. Argyros. Fire: Fundus image registration dataset. Journal for Modeling in Ophthalmology, vol. 1, no. 4, pp. 16–28, 2017.
    [69]
    E. J. Carmona, M. Rincón, J. García-Feijoó, J. M. Martínez-de-la-Casa. Identification of the optic nerve head with genetic algorithms. Artificial Intelligence in Medicine, vol. 43, no. 3, pp. 243–259, 2008. DOI: 10.1016/j.artmed.2008.04.005.
    [70]
    P. Porwal, S. Pachade, R. Kamble, M. Kokare, G. Deshmukh, V. Sahasrabuddhe, F. Meriaudeau. Indian diabetic retinopathy image dataset (IDRiD): A database for diabetic retinopathy screening research. Data, vol. 3, no. 3, Article number 25, 2018. DOI: 10.3390/data3030025.
    [71]
    T. Mahmudi, R. Kafieh, H. Rabbani, A. M. Dehnavi, M. Akhlagi. Comparison of macular OCTs in right and left eyes of normal people. In Proceedings of SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, SPIE, San Diego, USA, pp. 472–477, 2014. DOI: 10.1117/12.2044046.
    [72]
    H. Q. Li, J. H. Lim, J. Liu, P. Mitchell, A. G. Tan, J. J. Wang, T. Y. Wong. A computer-aided diagnosis system of nuclear cataract. IEEE Transactions on Biomedical Engineering, vol. 57, no. 7, pp. 1690–1698, 2010. DOI: 10.1109/TBME.2010.2041454.
    [73]
    W. Huang, K. L. Chan, H. Q. Li, J. H. Lim, J. Liu, T. Y. Wong. A computer assisted method for nuclear cataract grading from slit-lamp images using ranking. IEEE Transactions on Medical Imaging, vol. 30, no. 1, pp. 94–107, 2011. DOI: 10.1109/TMI.2010.2062197.
    [74]
    H. Q. Li, J. H. Lim, J. Liu, T. Y. Wong. Towards automatic grading of nuclear cataract. In Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Lyon, France, pp. 4961–4964, 2007. DOI: 10.1109/IEMBS.2007.4353454.
    [75]
    M. Caixinha, J. Amaro, M. Santos, F, Perdigão, M. Gomes, J. Santos. In-vivo automatic nuclear cataract detection and classification in an animal model by ultrasounds. IEEE Transactions on Biomedical Engineering, vol. 63, no. 11, pp. 2326–2335, 2016. DOI: 10.1109/TBME.2016.2527787.
    [76]
    J. W. Jiang, X. Y. Liu, K. Zhang, E. P. Long, L. M. Wang, W. T. Li, L. Liu, S. Wang, M. M. Zhu, J. T. Cui, Z. Z. Liu, Z. L. Lin, X. Y. Li, J. J. Chen, Q. Z. Cao, J. Li, X. H. Wu, D. N. Wang, J. H. Wang, H. T. Lin. Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network. BioMedical Engineering OnLine, vol. 16, no. 1, Article number 132, 2017. DOI: 10.1186/s12938-017-0420-1.
    [77]
    L. M. Wang, K. Zhang, X. Y. Liu, E. P. Long, J. W. Jiang, Y. Y. An, J. Zhang, Z. Z. Liu, Z. L. Lin, X. Y. Li, J. J. Chen, Q. Z. Cao, J. Li, X. H. Wu, D. N. Wang, W. T. Li, H. T. Lin. Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images. Scientific Reports, vol. 7, Article number 41545, 2017. DOI: 10.1038/srep41545.
    [78]
    J. Cheng. Sparse range-constrained learning and its application for medical image grading. IEEE Transactions on Medical Imaging, vol. 37, no. 12, pp. 2729–2738, 2018. DOI: 10.1109/TMI.2018.2851607.
    [79]
    A. B. Jagadale, S. S. Sonavane, D. V. Jadav. Computer aided system for early detection of nuclear cataract using circle Hough transform. In Proceedings of the 3rd International Conference on Trends in Electronics and Informatics, IEEE, Tirunelveli, India, pp. 1009–1012, 2019. DOI: 10.1109/ICOEI.2019.8862595.
    [80]
    K. Zhang, X. Y. Liu, J. W. Jiang, W. T. Li, S. Wang, L. Liu, X. J. Zhou, L. M. Wang. Prediction of postoperative complications of pediatric cataract patients using data mining. Journal of Translational Medicine, vol. 17, no. 1, Article number 2, 2019. DOI: 10.1186/s12967-018-1758-2.
    [81]
    H. Q. Li, L. Ko, J. H. Lim, J. Liu, D. W. K. Wong, T. Y. Wong, Y. Sun. Automatic opacity detection in retro-illumination images for cortical cataract diagnosis. In Proceedings of IEEE International Conference on Multimedia and Expo, IEEE, Hannover, Germany, pp. 553–556, 2008. DOI: 10.1109/ICME.2008.4607494.
    [82]
    Y. C. Chow, X. T. Gao, H. Q. Li, J. H. Lim, Y. Sun, T. Y. Wong. Automatic detection of cortical and PSC cataracts using texture and intensity analysis on retro-illumination lens images. In Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Boston, USA, pp. 5044–5047, 2011. DOI: 10.1109/IEMBS.2011.6091249.
    [83]
    W. J. Zhang, H. Q. Li. Lens opacity detection for serious posterior subcapsular cataract. Medical &Biological Engineering &Computing, vol. 55, no. 5, pp. 769–779, 2017. DOI: 10.1007/s11517-016-1554-1.
    [84]
    H. Q. Li, L. Ko, J. H. Lim, J. Liu, D. W. K. Wong, T. Y. Wong. Image based diagnosis of cortical cataract. In Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Vancouver, Canada, pp. 3904–3907, 2008. DOI: 10.1109/IEMBS.2008.4650063.
    [85]
    M. Caxinha, E. Velte, M. Santos, F. Perdigão, J. Amaro, M. Gomes, J. Santos. Automatic cataract classification based on ultrasound technique using machine learning: A comparative study. Physics Procedia, vol. 70, pp. 1221–1224, 2015. DOI: 10.1016/j.phpro.2015.08.263.
    [86]
    M. Caixinha, E. Velte, M. Santos, J. B. Santos. New approach for objective cataract classification based on ultrasound techniques using multiclass SVM classifiers. In Proceedings of IEEE International Ultrasonics Symposium, IEEE, Chicago, USA, pp. 2402–2405, 2014. DOI: 10.1109/ULTSYM.2014.0599.
    [87]
    M. Caixinha, D. A. Jesus, E. Velte, M. J. Santos, J. B. Santos. Using ultrasound backscattering signals and nakagami statistical distribution to assess regional cataract hardness. IEEE Transactions on Biomedical Engineering, vol. 61, no. 12, pp. 2921–2929, 2014. DOI: 10.1109/TBME.2014.2335739.
    [88]
    D. Jesus, E. Velte, M. Caixinha, M. Santos, J. Santos. Using of the ultrasound frequency dependent attenuation and Nakagami distribution for cataract evaluation. In Proceedings of the 3rd IEEE Portuguese Meeting in Bioengineering, IEEE, Braga, Portugal, 2013. DOI: 10.1109/ENBENG.2013.6518388.
    [89]
    Y. N. Fuadah, A. W. Setiawan, T. L. R. Mengko, Budiman. Mobile cataract detection using optimal combination of statistical texture analysis. In Proceedings of the 4th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, IEEE, Bandung, Indonesia, pp. 232–236, 2015. DOI: 10.1109/ICICI-BME.2015.7401368.
    [90]
    L. Y. Guo, J. J. Yang, L. H. Peng, J. Q. Li, Q. F. Liang. A computer-aided healthcare system for cataract classification and grading based on fundus image analysis. Computers in Industry, vol. 69, pp. 72–80, 2015. DOI: 10.1016/j.compind.2014.09.005.
    [91]
    W. M. Fan, R. F. Shen, Q. Q. Zhang, J. J. Yang, J. Q. Li. Principal component analysis based cataract grading and classification. In Proceedings of the 17th International Conference on E-health Networking, Application & Services, IEEE, Boston, USA, pp. 459–462, 2015. DOI: 10.1109/HealthCom.2015.7454545.
    [92]
    H. Y. Zhang, K. Niu, Y. M. Xiong, W. H. Yang, Z. Q. He, H. X. Song. Automatic cataract grading methods based on deep learning. Computer Methods and Programs in Biomedicine, vol. 182, Article number 104978, 2019. DOI: 10.1016/j.cmpb.2019.07.006.
    [93]
    C. S. Huo, F. Akhtar, P. Z. Li. A novel grading method of cataract based on AWM. In Proceedings of the IEEE 43rd Annual Computer Software and Applications Conference, IEEE, Milwaukee, USA, pp. 368–373, 2019. DOI: 10.1109/COMPSAC.2019.10234.
    [94]
    W. A. Song, P. Wang, X. D. Zhang, Q. Wang. Semi-supervised learning based on cataract classification and grading. In Proceedings of the 40th IEEE Annual Computer Software and Applications Conference, IEEE, Atlanta, USA, pp. 641–646, 2016. DOI: 10.1109/COMPSAC.2016.227.
    [95]
    W. A. Song, Y. Cao, Z. Q. Qiao, Q. Wang, J. J. Yang. An improved semi-supervised learning method on cataract fundus image classification. In Proceedings of the 43rd IEEE Annual Computer Software and Applications Conference, IEEE, Milwaukee, USA, pp. 362–367, 2019. DOI: 10.1109/COMPSAC.2019.10233.
    [96]
    X. Q. Zhang, J. S. Fang, Z. J. Xiao, R. Higashita, W. Chen, J. Yuan, J. Liu. Classification algorithm of nuclear cataract based on anterior segment coherence tomography image. Computer Science, vol. 49, no. 3, pp. 204–210, 2022. DOI: 10.11896/jsjkx.201100085. (in Chinese)
    [97]
    Y. Zhou, G. Q. Li, H. Q. Li. Automatic cataract classification using deep neural network with discrete state transition. IEEE Transactions on Medical Imaging, vol. 39, no. 2, pp. 436–446, 2020. DOI: 10.1109/TMI.2019.2928229.
    [98]
    X. T. Gao, S. Lin, T. Y. Wong. Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Transactions on Biomedical Engineering, vol. 62, no. 11, pp. 2693–2701, 2015. DOI: 10.1109/TBME.2015.2444389.
    [99]
    X. Y. Liu, J. W. Jiang, K. Zhang, E. P. Long, J. T. Cui, M. M. Zhu, Y. Y. An, J. Zhang, Z. Z. Liu, Z. L. Lin, X. Y. Li, J. J. Chen, Q. Z. Cao, J. Li, X. H. Wu, D. N. Wang, H. T. Lin. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network. PLoS One, vol. 12, no. 3, Article number 0168606, 2017. DOI: 10.1371/journal.pone.0168606.
    [100]
    K. Zhang, X. Y. Liu, F. Liu, L. He, L. Zhang, Y. H. Yang, W. T. Li, S. Wang, L. Liu, Z. Z. Liu, X. H. Wu, H. T. Lin. An interpretable and expandable deep learning diagnostic system for multiple ocular diseases: Qualitative study. Journal of Medical Internet Research, vol. 20, no. 11, Article number e11144, 2018. DOI: 10.2196/11144.
    [101]
    J. W. Jiang, X. Y. Liu, L. Liu, S. Wang, E. P. Long, H. Q. Yang, F. Q. Yuan, D. Y. Yu, K. Zhang, L. M. Wang, Z. Z. Liu, D. N. Wang, C. Z. Xi, Z. L. Lin, X. H. Wu, J. T. Cui, M. M. Zhu, H. T. Lin. Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network. PLoS One, vol. 13, no. 7, Article number 0201142, 2018. DOI: 10.1371/journal.pone.0201142.
    [102]
    C. X. Xu, X. J. Zhu, W. W. He, Y. Lu, X. X. He, Z. J. Shang, J. Wu, K. K. Zhang, Y. L. Zhang, X. F. Rong, Z. N. Zhao, L. Cai, A. Y. Ding, X. R. Li. Fully deep learning for slit-lamp photo based nuclear cataract grading. In Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, Shenzhen, China, pp. 513–521, 2019. DOI: 10.1007/978-3-030-32251-9_56.
    [103]
    D. S. J. Ting, M. Ang, J. S. Mehta, D. S. W. Ting. Artificial intelligence-assisted telemedicine platform for cataract screening and management: A potential model of care for global eye health. British Journal of Ophthalmology, vol. 103, no. 11, pp. 1537–1538, 2019. DOI: 10.1136/bjophthalmol-2019-315025.
    [104]
    D. Kim, T. J. Jun, Y. Eom, C. Kim, D. Kim. Tournament based ranking CNN for the cataract grading. In Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Berlin, Germany, pp. 1630–1636, 2019. DOI: 10.1109/EMBC.2019.8856636.
    [105]
    X. H. Wu, Y. L. Huang, Z. Z. Liu, W. Y. Lai, E. P. Long, K. Zhang, J. W. Jiang, D. R. Lin, K. X. Chen, T. Y. Yu, D. X. Wu, C. Li, Y. Y. Chen, M. J. Zou, C. Chen, Y. Zhu, C. Guo, X. Y. Zhang, R. X. Wang, Y. H. Yang, Y. F. Xiang, L. J. Chen, C. X. Liu, J. H. Xiong, Z. Y. Ge, D. D. Wang, G. H. Xu, S. L. Du, C. Xiao, J. H. Wu, K. Zhu, D. Y. Nie, F. Xu, J. Lv, W. R. Chen, Y. Z. Liu, H. T. Lin. Universal artificial intelligence platform for collaborative management of cataracts. British Journal of Ophthalmology, vol. 103, no. 11, pp. 1553–1560, 2019. DOI: 10.1136/bjophthalmol-2019-314729.
    [106]
    S. M. Hu, X. T. Wang, H. Wu, X. Z. Luan, P. Qi, Y. Lin, X. D. He, W. He. Unified diagnosis framework for automated nuclear cataract grading based on smartphone slit-lamp images. IEEE Access, vol. 8, pp. 174169–174178, 2020. DOI: 10.1109/ACCESS.2020.3025346.
    [107]
    J. W. Jiang, L. M. Wang, H. R. Fu, E. P. Long, Y. B. Sun, R. Y. Li, Z. W. Li, M. M. Zhu, Z. Z. Liu, J. J. Chen, Z. L. Lin, X. H. Wu, D. N. Wang, X. Y. Liu, H. T. Lin. Automatic classification of heterogeneous slit-illumination images using an ensemble of cost-sensitive convolutional neural networks. Annals of Translational Medicine, vol. 9, no. 7, Article number 550, 2021. DOI: 10.21037/atm-20-6635.
    [108]
    S. M. Hu, X. Z. Luan, H. Wu, X. T. Wang, C. H. Yan, J. Y. Wang, G. T. Liu, W. He. ACCV: Automatic classification algorithm of cataract video based on deep learning. BioMedical Engineering OnLine, vol. 20, no. 1, Article number 78, 2021. DOI: 10.1186/s12938-021-00906-3.
    [109]
    H. R. M. Tawfik, R. A. K. Birry, A. A. Saad. Early recognition and grading of cataract using a combined log Gabor/discrete wavelet transform with ANN and SVM. International Journal of Computer and Information Engineering, vol. 12, no. 12, pp. 1038–1043, 2018. DOI: 10.5281/zenodo.2022731.
    [110]
    X. F. Zhang, J. C. Lv, H. Zheng, Y. S. Sang. Attention-based multi-model ensemble for automatic cataract detection in B-scan eye ultrasound images. In Proceedings of International Joint Conference on Neural Networks, IEEE, Glasgow, UK, 2020. DOI: 10.1109/IJCNN48605.2020.9207696.
    [111]
    H. J. Wu, J. C. Lv, J. Wang. Automatic cataract detection with multi-task learning. In Proceedings of International Joint Conference on Neural Networks, IEEE, Shenzhen, China, 2021. DOI: 10.1109/IJCNN52387.2021.9533424.
    [112]
    L. L. Zhang, J. Q. Li, I. Zhang, H. Han, B. Liu, J. J. Yang, Q. Wang. Automatic cataract detection and grading using deep convolutional neural network. In Proceedings of the 14th IEEE International Conference on Networking, Sensing and Control, IEEE, Calabria, Italy, pp. 60–65, 2017. DOI: 10.1109/ICNSC.2017.8000068.
    [113]
    Y. Y. Dong, Q. Y. Zhang, Z. Q. Qiao, J. J. Yang. Classification of cataract fundus image based on deep learning. In Proceedings of IEEE International Conference on Imaging Systems and Techniques, IEEE, Beijing, China, 2017. DOI: 10.1109/IST.2017.8261463.
    [114]
    X. Xu, L. L. Zhang, J. Q. Li, Y. Guan, L. Zhang. A hybrid global-local representation CNN model for automatic cataract grading. IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 2, pp. 556–567, 2020. DOI: 10.1109/JBHI.2019.2914690.
    [115]
    T. Pratap, P. Kokil. Efficient network selection for computer-aided cataract diagnosis under noisy environment. Computer Methods and Programs in Biomedicine, vol. 200, Article number 105927, 2021. DOI: 10.1016/j.cmpb.2021.105927.
    [116]
    A. Imran, J. Q. Li, Y. Pei, F. Akhtar, T. Mahmood, L. Zhang. Fundus image-based cataract classification using a hybrid convolutional and recurrent neural network. The Visual Computer, vol. 37, no. 8, pp. 2407–2417, 2021. DOI: 10.1007/s00371-020-01994-3.
    [117]
    M. S. Junayed, M. B. Islam, A. Sadeghzadeh, S. Rahman. CataractNet: An automated cataract detection system using deep learning for fundus images. IEEE Access, vol. 9, pp. 128799–128808, 2021. DOI: 10.1109/ACCESS.2021.3112938.
    [118]
    Y. C. Tham, J. H. L. Goh, A. Anees, X. F. Lei, T. H. Rim, M. L. Chee, Y. X. Wang, J. B. Jonas, S. Thakur, Z. L. Teo, N. Cheung, H. Hamzah, G. S. W. Tan, R. Husain, C. Sabanayagam, J. J. Wang, Q. Y. Chen, Z. Y. Lu, T. D. Keenan, E. Y. Chew, A. G. Tan, P. Mitchell, R. S. M. Goh, X. X. Xu, Y. Liu, T. Y. Wong, C. Y. Cheng. Detecting visually significant cataract using retinal photograph-based deep learning. Nature Aging, vol. 2, no. 3, pp. 264–271, 2022. DOI: 10.1038/s43587-022-00171-6.
    [119]
    X. Q. Zhang, Z. J. Xiao, R. Higashita, W. Chen, J. Yuan, J. S. Fang, Y. Hu, J. Liu. A novel deep learning method for nuclear cataract classification based on anterior segment optical coherence tomography images. In Proceedings of IEEE International Conference on Systems, IEEE, Toronto, Canada, pp. 662–668, 2020. DOI: 10.1109/SMC42975.2020.9283218.
    [120]
    Z. Xiao, X. Zhang, R. Higashita, W. Chen, J. Yuan, J. Liu. A 3D CNN-based multi-task learning for cataract screening and left and right eye classification on 3D AS-OCT images. In Proceedings of the 3rd International Conference on Intelligent Medicine and Health, ACM, Macau, China, pp. 1–7, 2021. DOI: 10.1145/3484377.3484378.
    [121]
    Z. J. Xiao, X. Q. Zhang, R. Higashita, Y. Hu, J. Yuan, W. Chen, J. Liu. Gated channel attention network for cataract classification on AS-OCT image. In Proceedings of the 28th International Conference on Neural Information Processing, Springer, Sanur, Indonesia, pp. 357–368, 2021. DOI: 10.1007/978-3-030-92238-2_30.
    [122]
    X. Q. Zhang, Z. J. Xiao, X. L. Li, X. Wu, H. X. Sun, J. Yuan, R. Higashita, J. Liu. Mixed pyramid attention network for nuclear cataract classification based on anterior segment OCT images. Health Information Science and Systems, vol. 10, no. 1, Article number 3, 2022. DOI: 10.1007/s13755-022-00170-2.
    [123]
    I. Tolstikhin, N. Houlsby, A. Kolesnikov, L. Beyer, X. H. Zhai, T. Unterthiner, J. Yung, A. Steiner, D. Keysers, J. Uszkoreit, M. Lucic, A. Dosovitskiy. MLP-mixer: An all-MLP architecture for vision. In Proceedings of the 35th Conference on Neural Information Processing Systems, 2021.
    [124]
    D. Z. Lian, Z. H. Yu, X. Sun, S. H. Gao. AS-MLP: An axial shifted MLP architecture for vision. [Online], Available: https://arxiv.org/abs/2107.08391, 2021.
    [125]
    Y. Mansour, K. Lin, R. Heckel. Image-to-image MLP-mixer for image reconstruction. [Online], Available: https://arxiv.org/abs/2202.02018, 2021.
    [126]
    H. Touvron, P. Bojanowski, M. Caron, M. Cord, A. El-Nouby, E. Grave, G. Izacard, A. Joulin, G. Synnaeve, J. Verbeek, H. Jégou. ResMLP: Feedforward networks for image classification with data-efficient training. [Online], Available: https://arxiv.org/abs/2105.03404, 2021.
    [127]
    H. J. Zheng, P. C. He, W. Z. Chen, M. Y. Zhou. Mixing and shifting: Exploiting global and local dependencies in vision MLPs. [Online], Available: https://arxiv.org/abs/2202.06510, 2022.
    [128]
    C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich. Going deeper with convolutions. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Boston, USA, pp. 1–9, 2015. DOI: 10.1109/CVPR.2015.7298594.
    [129]
    A. Krizhevsky, I. Sutskever, G. E. Hinton. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems, Curran Associates Inc, Lake Tahoe, USA, pp. 1097–1105, 2012.
    [130]
    H. Chen, Q. Dou, D. Ni, J. Z. Cheng, J. Qin, S. L. Li, P. A. Heng. Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks. In Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Munich, Germany, pp. 507–514, 2015. DOI: 10.1007/978-3-319-24553-9_62.
    [131]
    H. Z. Fu, Y. W. Xu, S. Lin, D. W. K. Wong, J. Liu. DeepVessel: Retinal vessel segmentation via deep learning and conditional random field. In Proceedings of the 19th International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, Athens, Greece, pp. 132–139, 2016. DOI: 10.1007/978-3-319-46723-8_16.
    [132]
    M. D. Abràmoff, Y. Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, M. Niemeijer. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative Ophthalmology &Visual Science, vol. 57, no. 13, pp. 5200–5206, 2016. DOI: 10.1167/iovs.16-19964.
    [133]
    G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. Van Der Laak, B. Van Ginneken, C. I. Sánchez. A survey on deep learning in medical image analysis. Medical Image Analysis, vol. 42, pp. 60–88, 2017. DOI: 10.1016/j.media.2017.07.005.
    [134]
    A. Mansoor, J. J. Cerrolaza, R. Idrees, E. Biggs, M. A. Alsharid, R. A. Avery, M. G. Linguraru. Deep learning guided partitioned shape model for anterior visual pathway segmentation. IEEE Transactions on Medical Imaging, vol. 35, no. 8, pp. 1856–1865, 2016. DOI: 10.1109/TMI.2016.2535222.
    [135]
    S. N. Xie, R. Girshick, P. Dollár, Z. W. Tu, K. M. He. Aggregated residual transformations for deep neural networks. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, USA, pp. 1492–1500, 2017. DOI: 10.1109/CVPR.2017.634.
    [136]
    C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna. Rethinking the inception architecture for computer vision. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas, USA, pp. 2818–2826, 2016. DOI: 10.1109/CVPR.2016.308.
    [137]
    G. Huang, Z. Liu, L. Van Der Maaten, K. Q. Weinberger. Densely connected convolutional networks. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, USA, pp. 2261–2269, 2017. DOI: 10.1109/CVPR.2017.243.
    [138]
    X. Y. Zhang, X. Y. Zhou, M. X. Lin, J. Sun. ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Salt Lake City, USA, pp. 6848–6856, 2018. DOI: 10.1109/CVPR.2018.00716.
    [139]
    P. S. Grewal, F. Oloumi, U. Rubin, M. T. S. Tennant. Deep learning in ophthalmology: A review. Canadian Journal of Ophthalmology, vol. 53, no. 4, pp. 309–313, 2018. DOI: 10.1016/j.jcjo.2018.04.019.
    [140]
    L. Deng, P. Jiao, J. Pei, Z. Z. Wu, G. Q. Li. GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework. Neural Networks, vol. 100, pp. 49–58, 2018. DOI: 10.1016/j.neunet.2018.01.010.
    [141]
    P. S. Yin, M. K. Tan, H. Q. Min, Y. W. Xu, G. H. Xu, Q. Y. Wu, Y. F. Tong, H. Risa, J. Liu. Automatic segmentation of cortex and nucleus in anterior segment OCT images. In Proceedings of the 1st International Workshop on Computational Pathology and Ophthalmic Medical Image Analysis, Springer, Granada, Spain, pp. 269–276, 2018. DOI: 10.1007/978-3-030-00949-6_32.
    [142]
    S. H. Zhang, Y. G. Yan, P. S. Yin, Z. Qiu, W. Zhao, G. P. Cao, W. Chen, J. Yuan, R. Higashita, Q. Y. Wu, M. K. Tan, J. Liu. Guided M-Net for high-resolution biomedical image segmentation with weak boundaries. In Proceedings of the 6th International Workshop on Ophthalmic Medical Image Analysis, Springer, Shenzhen, China, pp. 43–51, 2019. DOI: 10.1007/978-3-030-32956-3_6.
    [143]
    G. P. Cao, W. Zhao, R. Higashita, J. Liu, W. Chen, J. Yuan, Y. B. Zhang, M. Yang. An efficient lens structures segmentation method on AS-OCT images. In Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, IEEE, Montreal, Canada, pp. 1646–1649, 2020. DOI: 10.1109/EMBC44109.2020.9175944.
    [144]
    Y. Hu, X. Q. Zhang, L. Xu, F. X. He, Z. Tian, W. She, W. Liu. Harmonic loss function for sensor-based human activity recognition based on LSTM recurrent neural networks. IEEE Access, vol. 8, pp. 135617–135627, 2020. DOI: 10.1109/ACCESS.2020.3003162.
    [145]
    M. Choi, H. Kim, B. Han, N. Xu, K. M. Lee. Channel attention is all you need for video frame interpolation. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 7, pp. 10663–10671, 2020. DOI: 10.1609/aaai.v34i07.6693.
    [146]
    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, USA, pp. 6000–6010, 2017.
    [147]
    Z. J. Zhang, H. Z. Fu, H. Dai, J. B. Shen, Y. W. Pang, L. Shao. ET-Net: A generic edge-attention guidance network for medical image segmentation. In Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, Shenzhen, China, pp. 442–450, 2019. DOI: 10.1007/978-3-030-32239-7_49.
    [148]
    M. H. Guo, T. X. Xu, J. J. Liu, Z. N. Liu, P. T. Jiang, T. J. Mu, S. H. Zhang, R. R. Martin, M. M. Cheng, S. M. Hu. Attention mechanisms in computer vision: A survey. Computational Visual Media, to be published. DOI: 10.1007/s41095-022-0271-y.
    [149]
    X. L. Wang, R. Girshick, A. Gupta, K. M. He. Non-local neural networks. In Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Salt Lake City, USA, pp. 7794–7803, 2018. DOI: 10.1109/CVPR.2018.00813.
    [150]
    W. H. Yu, M. Luo, P. Zhou, C. Y. Si, Y. C. Zhou, X. C. Wang, J. S. Feng, S. C. Yan. MetaFormer is actually what you need for vision. [Online], Available: https://arxiv.org/abs/2111.11418, 2021.
    [151]
    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. H. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In Proceedings of the 9th International Conference on Learning Representations, 2021.
    [152]
    K. L. He, C. Gan, Z. Y. Li, I. Rekik, Z. H. Yin, W. Ji, Y. Gao, Q. Wang, J. F. Zhang, D. G. Shen. Transformers in medical image analysis: A review. [Online], Available: https://arxiv.org/abs/2202.12165, 2022.
    [153]
    A. Gulati, J. Qin, C. C. Chiu, N. Parmar, Y. Zhang, J. H. Yu, W. Han, S. B. Wang, Z. D. Zhang, Y. H. Wu, R. M. Pang. Conformer: Convolution-augmented transformer for speech recognition. In Proceedings of the 21st Annual Conference of the International Speech Communication Association, Shanghai, China, pp. 5036–5040, 2020.
    [154]
    C. Shorten, T. M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data, vol. 6, no. 1, Article number 60, 2019. DOI: 10.1186/s40537-019-0197-0.
    [155]
    C. Y. I. Cheung, H. Q. Li, E. L. Lamoureux, P. Mitchell, J. J. Wang, A. G. Tan, L. K. Johari, J. Liu, J. H. Lim, T. Aung, T. Y. Wong. Validity of a new computer-aided diagnosis imaging program to quantify nuclear cataract from slit-lamp photographs. Investigative Ophthalmology &Visual Science, vol. 52, no. 3, pp. 1314–1319, 2011. DOI: 10.1167/iovs.10-5427.
    [156]
    H. Q. Li, J. H. Lim, J. Liu, D. W. K. Wong, T. Y. Wong. Computer aided diagnosis of nuclear cataract. In Proceedings of the 3rd IEEE Conference on Industrial Electronics and Applications, IEEE, Singapore, pp. 1841–1844, 2008. DOI: 10.1109/ICIEA.2008.4582838.
    [157]
    A. W. P. Foong, S. M. Saw, J. L. Loo, S. Shen, S. C. Loon, M. Rosman, T. Aung, D. T. H. Tan, E. S. Tai, T. Y. Wong. Rationale and methodology for a population-based study of eye diseases in Malay people: The Singapore Malay eye study (SiMES). Ophthalmic Epidemiology, vol. 14, no. 1, pp. 25–35, 2007. DOI: 10.1080/09286580600878844.
    [158]
    S. Resnikoff, V. C. Lansingh, L. Washburn, W. Felch, T. M. Gauthier, H. R. Taylor, K. Eckert, D. Parke, P. Wiedemann. Estimated number of ophthalmologists worldwide (international council of ophthalmology update): Will we meet the needs? British Journal of Ophthalmology, vol. 104, no. 4, pp. 588–592, 2020. DOI: 10.1136/bjophthalmol-2019-314336.
    [159]
    S. Resnikoff, W. Felch, T. M. Gauthier, B. Spivey. The number of ophthalmologists in practice and training worldwide: A growing gap despite more than 200000 practitioners. British Journal of Ophthalmology, vol. 96, no. 6, pp. 783–787, 2012. DOI: 10.1136/bjophthalmol-2011-301378.
    [160]
    S. Ravi, H. Larochelle. Optimization as a model for few-shot learning. In Proceedings of the 5th International Conference on Learning Representations, Toulon, France, 2016.
    [161]
    N. Grira, M. Crucianu, N. Boujemaa. Unsupervised and semi-supervised clustering: A brief survey. A review of Machine Learning Techniques for Processing Multimedia Content, 2004.
    [162]
    S. Fogel, H. Averbuch-Elor, D. Cohen-Or, J. Goldberger. Clustering-driven deep embedding with pairwise constraints. IEEE Computer Graphics and Applications, vol. 39, no. 4, pp. 16–27, 2019. DOI: 10.1109/MCG.2018.2881524.
    [163]
    X. F. Guo, X. W. Liu, E. Zhu, J. P. Yin. Deep clustering with convolutional autoencoders. In Proceedings of the 24th International Conference on Neural Information Processing, Springer, Guangzhou, China, pp. 373–382, 2017. DOI: 10.1007/978-3-319-70096-0_39.
    [164]
    Y. C. Hsu, Z. Kira. Neural network-based clustering using pairwise constraints. [Online], Available: https://arxiv.org/abs/1511.06321, 2015
    [165]
    B. Ramamurthy, K. R. Chandran. Content based image retrieval for medical images using canny edge detection algorithm. International Journal of Computer Applications, vol. 17, no. 6, pp. 32–37, 2011. DOI: 10.5120/2222-2831.
    [166]
    J. Sivakamasundari, G. Kavitha, V. Natarajan, S. Ramakrishnan. Proposal of a content based retinal image retrieval system using kirsch template based edge detection. In Proceedings of International Conference on Informatics, Electronics & Vision, IEEE, Dhaka, Bangladesh, 2014. DOI: 10.1109/ICIEV.2014.6850744.
    [167]
    Y. F. Fathabad, M. A. Balafar. Content based image retrieval for medical images. International Journal on Technical and Physical Problems of Engineering, vol. 4, no. 12, pp. 177–182, 2012.
    [168]
    J. S. Fang, Y. W. Xu, X. Q. Zhang, Y. Hu, J. Liu. Attention-based saliency hashing for ophthalmic image retrieval. In Proceedings of IEEE International Conference on Bioinformatics and Biomedicine, IEEE, Seoul, Korea, pp. 990–995, 2020. DOI: 10.1109/BIBM49941.2020.9313536.
    [169]
    D. R. Lin, J. J. Chen, Z. L. Lin, X. Y. Li, K. Zhang, X. H. Wu, Z. Z. Liu, J. L. Huang, J. Li, Y. Zhu, C. Chen, L. Q. Zhao, Y. F. Xiang, C. Guo, L. M. Wang, Y. Z. Liu, W. R. Chen, H. T. Lin. A practical model for the identification of congenital cataracts using machine learning. EBioMedicine, vol. 51, Article number 102621, 2020. DOI: 10.1016/j.ebiom.2019.102621.
    [170]
    M. M. Yang, J. J. Yang, Q. Y. Zhang, Y. Niu, J. Q. Li. Classification of retinal image for automatic cataract detection. In Proceedings of the 15th IEEE International Conference on e-Health Networking, Applications and Services, IEEE, Lisbon, Portugal, pp. 674–679, 2013. DOI: 10.1109/HealthCom.2013.6720761.
    [171]
    D. Stoyanov, Z. Taylor, G. Carneiro, T. Syeda-Mahmood, A. Martel, L. Maier-Hein, J. M. R. S. Tavares, A. Bradley, J. P. Papa, V. Belagiannis, J. C. Nascimento, Z. Lu, S. Conjeti, M. Moradi, H. Greenspan, A. Madabhushi. Deep Learning In Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Cham, Germany: Springer, 2017. DOI: 10.1007/978-3-030-00889-5.
    [172]
    J. Q. Ma, Z. Zhao, J. L. Chen, A. Li, L. C. Hong, E. H. Chi. SNR: Sub-network routing for flexible parameter sharing in multi-task learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Hawaii, USA, pp. 216–223, 2019.
    [173]
    Z. Guo, X. Li, H. Huang, N. Guo, Q. Z. Li. Deep learning-based image segmentation on multimodal medical imaging. IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 3, no. 2, pp. 162–169, 2019. DOI: 10.1109/TRPMS.2018.2890359.
    [174]
    B. Cheng, M. X. Liu, H. I. Suk, D. G. Shen, D. Q. Zhang, A. S. D. N. Initiative. Multimodal manifold-regularized transfer learning for MCI conversion prediction. Brain Imaging and Behavior, vol. 9, no. 4, pp. 913–926, 2015. DOI: 10.1007/s11682-015-9356-x.
    [175]
    S. F. Mohammadi, M. Sabbaghi, H. Z-Mehrjardi, H. Hashemi, S. Alizadeh, M. Majdi, F. Taee. Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification. Journal of Cataract &Refractive Surgery, vol. 38, no. 3, pp. 403–408, 2012. DOI: 10.1016/j.jcrs.2011.09.036.
    [176]
    C. Guo, G. Pleiss, Y. Sun, K. Q. Weinberger. On calibration of modern neural networks. In Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, pp. 1321–1330, 2017.
    [177]
    R. Müller, S. Kornblith, G. Hinton. When does label smoothing help? In Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, Canada, Article number 422, 2019.
    [178]
    B. L. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba. Learning deep features for discriminative localization. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas, USA, pp. 2921–2929, 2016. DOI: 10.1109/CVPR.2016.319.
    [179]
    R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of IEEE International Conference on Computer Vision, IEEE, Venice, Italy, pp. 618–626, 2017. DOI: 10.1109/ICCV.2017.74.
    [180]
    R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, D. Pedreschi. A survey of methods for explaining black box models. ACM Computing Surveys, vol. 51, no. 5, Article number 93, 2018. DOI: 10.1145/3236009.
    [181]
    J. S. Lu, C. M. Xiong, D. Parikh, R. Socher. Knowing when to look: Adaptive attention via a visual sentinel for image captioning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, USA, pp. 3242–3250, 2017. DOI: 10.1109/CVPR.2017.345.
    [182]
    S. Kornblith, M. Norouzi, H. Lee, G. E. Hinton. Similarity of neural network representations revisited. In Proceedings of the 36th International Conference on Machine Learning, Long Beach, USA, pp. 3519–3529, 2019.
    [183]
    R. Supriyanti, Y. Ramadhani. The achievement of various shapes of specular reflections for cataract screening system based on digital images. In Proceedings of International Conference on Biomedical Engineering and Technology, Kualalumpur, Malaysia, pp. 75–79, 2011.
    [184]
    R. Supriyanti, Y. Ramadhani. Consideration of iris characteristic for improving cataract screening techniques based on digital image. In Proceedings of the 2nd International Conference on Biomedical Engineering and Technology, Hong Kong, China, pp. 130–133, 2012.
    [185]
    J. Rana, S. M. Galib. Cataract detection using smartphone. In Proceedings of the 3rd International Conference on Electrical Information and Communication Technology, IEEE, Khulna, Bangladesh, 2017. DOI: 10.1109/EICT.2017.8275136.
    [186]
    Y. Cheng, D. Wang, P. Zhou, T. Zhang. A survey of model compression and acceleration for deep neural networks. [Online], Available: https://arxiv.org/abs/1710.09282, 2017.
    [187]
    Y. A. Jiang, S. Q. Wang, V. Valls, B. J. Ko, W. H. Lee, K. K. Leung, L. Tassiulas. Model pruning enables efficient federated learning on edge devices. In Proceedings of the 34th Conference on Neural Information Processing Systems, Vancouver, Canada, 2019.
    [188]
    Y. H. He, X. Y. Zhang, J. Sun. Channel pruning for accelerating very deep neural networks. In Proceedings of IEEE International Conference on Computer Vision, IEEE, Venice, Italy, pp. 1398–1406, 2017. DOI: 10.1109/ICCV.2017.155.
    [189]
    H. Song, M. Kim, D. Park, Y. Shin, J. G. Lee. Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems, to be published. DOI: 10.1109/TNNLS.2022.3152527.
    [190]
    J. X. Zhong, N. N. Li, W. J. Kong, S. Liu, T. H. Li, G. Li. Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Long Beach, USA, pp. 1237–1246, 2019. DOI: 10.1109/CVPR.2019.00133.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(13)  / Tables(2)

    用微信扫码二维码

    分享至好友和朋友圈

    Article Metrics

    Article views (659) PDF downloads(58) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return