{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T15:20:29Z","timestamp":1767972029698,"version":"3.49.0"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031189098","type":"print"},{"value":"9783031189104","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-18910-4_22","type":"book-chapter","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:03:53Z","timestamp":1666825433000},"page":"262-273","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Novel Local-Global Spatial Attention Network for\u00a0Cortical Cataract Classification in\u00a0AS-OCT"],"prefix":"10.1007","author":[{"given":"Zunjie","family":"Xiao","sequence":"first","affiliation":[]},{"given":"Xiaoqing","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qingyang","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Zhuofei","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Gelei","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yuan","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Risa","family":"Higashita","sequence":"additional","affiliation":[]},{"given":"Jiang","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"issue":"1","key":"22_CR1","first-page":"91","volume":"29","author":"G Ben\u010di\u0107","year":"2005","unstructured":"Ben\u010di\u0107, G., Zori\u0107-Geber, M., \u0160ari\u0107, D., \u010corak, M., Mandi\u0107, Z.: Clinical importance of the lens opacities classification system III (LOCS III) in phacoemulsification. Coll. Antropol. 29(1), 91\u201394 (2005)","journal-title":"Coll. Antropol."},{"issue":"9","key":"22_CR2","doi-asserted-by":"publisher","first-page":"e888","DOI":"10.1016\/S2214-109X(17)30293-0","volume":"5","author":"RR Bourne","year":"2017","unstructured":"Bourne, R.R., et al.: Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis. Lancet Glob. Health 5(9), e888\u2013e897 (2017)","journal-title":"Lancet Glob. Health"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Cao, G., et al.: An efficient lens structures segmentation method on AS-OCT images. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1646\u20131649. IEEE (2020)","DOI":"10.1109\/EMBC44109.2020.9175944"},{"key":"22_CR4","series-title":"Studies in Big Data","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/978-3-030-74575-2_14","volume-title":"Artificial Intelligence and Blockchain for Future Cybersecurity Applications","author":"K El Asnaoui","year":"2021","unstructured":"El Asnaoui, K., Chawki, Y., Idri, A.: Automated methods for detection and classification pneumonia based on X-Ray images using deep learning. In: Maleh, Y., Baddi, Y., Alazab, M., Tawalbeh, L., Romdhani, I. (eds.) Artificial Intelligence and Blockchain for Future Cybersecurity Applications. SBD, vol. 90, pp. 257\u2013284. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-74575-2_14"},{"issue":"4","key":"22_CR5","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1109\/JBHI.2022.3148317","volume":"26","author":"Y Feng","year":"2022","unstructured":"Feng, Y., Yang, X., Qiu, D., Zhang, H., Wei, D., Liu, J.: PCXRNet: pneumonia diagnosis from chest X-Ray images using condense attention block and multiconvolution attention block. IEEE J. Biomed. Health Inform. 26(4), 1484\u20131495 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"9","key":"22_CR6","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1109\/TMI.2017.2703147","volume":"36","author":"H Fu","year":"2017","unstructured":"Fu, H., et al.: Segmentation and quantification for angle-closure glaucoma assessment in anterior segment OCT. IEEE Trans. Med. Imaging 36(9), 1930\u20131938 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Gao, X., Li, H., Lim, J.H., Wong, T.Y.: Computer-aided cataract detection using enhanced texture features on retro-illumination lens images. In: 2011 18th IEEE International Conference on Image Processing, pp. 1565\u20131568. IEEE (2011)","DOI":"10.1109\/ICIP.2011.6115746"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Guo, J., et al.: SPANet: spatial pyramid attention network for enhanced image recognition. In: 2020 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/ICME46284.2020.9102906"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"22_CR10","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., Vedaldi, A.: Gather-excite: exploiting feature context in convolutional neural networks. In: Advances in Neural Information Processing Systems 31 (2018)"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"5","key":"22_CR12","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122\u20131131 (2018)","journal-title":"Cell"},{"issue":"2","key":"22_CR13","first-page":"129","volume":"13","author":"M Khalil","year":"2021","unstructured":"Khalil, M., Ayad, H., Adib, A.: MR-brain image classification system based on SWT-LBP and ensemble of SVMs. Int. J. Med. Eng. Inform. 13(2), 129\u2013142 (2021)","journal-title":"Int. J. Med. Eng. Inform."},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Lee, H., Kim, H.E., Nam, H.: SRM: a style-based recalibration module for convolutional neural networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1854\u20131862 (2019)","DOI":"10.1109\/ICCV.2019.00194"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Li, H., et al.: Structure-consistent restoration network for cataract fundus image enhancement. arXiv preprint arXiv:2206.04684 (2022)","DOI":"10.1007\/978-3-031-16434-7_47"},{"key":"22_CR16","doi-asserted-by":"publisher","first-page":"1699","DOI":"10.1109\/TMI.2022.3147854","volume":"41","author":"H Li","year":"2022","unstructured":"Li, H., et al.: An annotation-free restoration network for cataractous fundus images. IEEE Trans. Med. Imaging 41, 1699\u20131710 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Li, H., Ko, L., Lim, J.H., Liu, J., Wong, D.W.K., Wong, T.Y.: Image based diagnosis of cortical cataract. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3904\u20133907. IEEE (2008)","DOI":"10.1109\/IEMBS.2008.4650063"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Integrating research, clinical practice and translation: the Singapore experience. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7148\u20137151. IEEE (2013)","DOI":"10.1109\/EMBC.2013.6611206"},{"issue":"4","key":"22_CR19","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1111\/aos.13694","volume":"96","author":"NY Makhotkina","year":"2018","unstructured":"Makhotkina, N.Y., Berendschot, T.T., van den Biggelaar, F.J., Weik, A.R., Nuijts, R.M.: Comparability of subjective and objective measurements of nuclear density in cataract patients. Acta Ophthalmol. 96(4), 356\u2013363 (2018)","journal-title":"Acta Ophthalmol."},{"issue":"8","key":"22_CR20","doi-asserted-by":"publisher","first-page":"630","DOI":"10.4103\/0301-4738.169787","volume":"63","author":"JS Maslin","year":"2015","unstructured":"Maslin, J.S., Barkana, Y., Dorairaj, S.K.: Anterior segment imaging in glaucoma: an updated review. Indian J. Ophthalmol. 63(8), 630 (2015)","journal-title":"Indian J. Ophthalmol."},{"key":"22_CR21","unstructured":"Park, J., Woo, S., Lee, J.Y., Kweon, I.S.: BAM: bottleneck attention module. arXiv preprint arXiv:1807.06514 (2018)"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Qin, Z., Zhang, P., Wu, F., Li, X.: FcanNet: frequency channel attention networks. arXiv preprint arXiv:2012.11879 (2020)","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"22_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"key":"22_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1007\/978-3-030-92238-2_30","volume-title":"Neural Information Processing","author":"Z Xiao","year":"2021","unstructured":"Xiao, Z., et al.: Gated channel attention network for\u00a0cataract classification on\u00a0AS-OCT image. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. LNCS, vol. 13110, pp. 357\u2013368. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-92238-2_30"},{"key":"22_CR27","unstructured":"Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: International Conference on Machine Learning, pp. 2048\u20132057. PMLR (2015)"},{"key":"22_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1007\/978-3-319-46726-9_53","volume-title":"Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016","author":"Y Xu","year":"2016","unstructured":"Xu, Y., Duan, L., Wong, D.W.K., Wong, T.Y., Liu, J.: Semantic reconstruction-based nuclear cataract grading from slit-lamp lens images. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 458\u2013466. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46726-9_53"},{"key":"22_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, Q.L., Yang, Y.B.: SA-Net: shuffle attention for deep convolutional neural networks. In: 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2021, pp. 2235\u20132239. IEEE (2021)","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"22_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s11633-022-1329-0","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Hu, Y., Xiao, Z., Fang, J., Higashita, R., Liu, J.: Machine learning for cataract classification\/grading on ophthalmic imaging modalities: a survey. Mach. Intell. Res. (2022). https:\/\/doi.org\/10.1007\/s11633-022-1329-0","journal-title":"Mach. Intell. Res."},{"key":"22_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: A novel deep learning method for nuclear cataract classification based on anterior segment optical coherence tomography images. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 662\u2013668. IEEE (2020)","DOI":"10.1109\/SMC42975.2020.9283218"},{"issue":"1","key":"22_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-022-00170-2","volume":"10","author":"X Zhang","year":"2022","unstructured":"Zhang, X., et al.: Mixed pyramid attention network for nuclear cataract classification based on anterior segment OCT images. Health Inf. Sci. Syst. 10(1), 1\u201312 (2022)","journal-title":"Health Inf. Sci. Syst."},{"key":"22_CR33","doi-asserted-by":"publisher","first-page":"948","DOI":"10.11834\/jig.210735","volume":"27","author":"X Zhang","year":"2022","unstructured":"Zhang, X., et al.: Nuclear cataract classification based on multi-region fusion attention network model. J. Image Graph. 27, 948\u2013960 (2022). https:\/\/doi.org\/10.11834\/jig.210735","journal-title":"J. Image Graph."},{"issue":"2","key":"22_CR34","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1109\/TMI.2019.2928229","volume":"39","author":"Y Zhou","year":"2019","unstructured":"Zhou, Y., Li, G., Li, H.: Automatic cataract classification using deep neural network with discrete state transition. IEEE Trans. Med. Imaging 39(2), 436\u2013446 (2019)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18910-4_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T16:35:32Z","timestamp":1728232532000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18910-4_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031189098","9783031189104"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18910-4_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/en.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"564","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"233","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.03","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.35","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}