{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T08:47:49Z","timestamp":1743842869979,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031442155"},{"type":"electronic","value":"9783031442162"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-44216-2_8","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T07:02:58Z","timestamp":1695279778000},"page":"87-98","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Clinical Pixel Feature Recalibration Module for\u00a0Ophthalmic Image Classification"],"prefix":"10.1007","author":[{"given":"JiLu","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Xiaoqing","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"Wu","sequence":"additional","affiliation":[]},{"given":"ZhiXuan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Tong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Heng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Jiang","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","unstructured":"Das, V., Dandapat, S., Bora, P.K.: Multi-scale deep feature fusion for automated classification of macular pathologies from OCT images. Biomed. Signal Process. Control 54, 101605 (2019). https:\/\/doi.org\/10.1016\/j.bspc.2019.101605, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1746809419301867","DOI":"10.1016\/j.bspc.2019.101605"},{"issue":"7","key":"8_CR2","doi-asserted-by":"publisher","first-page":"3358","DOI":"10.1109\/TCYB.2019.2897162","volume":"50","author":"H Fu","year":"2019","unstructured":"Fu, H., et al.: Angle-closure detection in anterior segment oct based on multilevel deep network. IEEE Trans. Cybern. 50(7), 3358\u20133366 (2019)","journal-title":"IEEE Trans. Cybern."},{"key":"8_CR3","doi-asserted-by":"publisher","unstructured":"Guo, M.H., Liu, Z.N., Mu, T.J., Hu, S.M.: Beyond self-attention: external attention using two linear layers for visual tasks. T-PAMI 1\u201313 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2022.3211006","DOI":"10.1109\/TPAMI.2022.3211006"},{"issue":"2","key":"8_CR4","first-page":"254","volume":"41","author":"J Hao","year":"2021","unstructured":"Hao, J., et al.: Hybrid variation-aware network for angle-closure assessment in AS-OCT. TMI 41(2), 254\u2013265 (2021)","journal-title":"TMI"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"8_CR6","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., Vedaldi, A.: Gather-excite: exploiting feature context in convolutional neural networks. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS 2018, pp. 9423\u20139433. Curran Associates Inc., Red Hook (2018)"},{"key":"8_CR7","doi-asserted-by":"publisher","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00745","DOI":"10.1109\/CVPR.2018.00745"},{"key":"8_CR8","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images (2009)"},{"key":"8_CR9","doi-asserted-by":"publisher","unstructured":"Kumar, G., Chatterjee, S., Chattopadhyay, C.: DRISTI: a hybrid deep neural network for diabetic retinopathy diagnosis. Signal Image Video Process. 15(8), 1679\u20131686 (2021). https:\/\/doi.org\/10.1007\/s11760-021-01904-7, https:\/\/europepmc.org\/articles\/PMC8051933","DOI":"10.1007\/s11760-021-01904-7"},{"key":"8_CR10","first-page":"1699","volume":"41","author":"H Li","year":"2022","unstructured":"Li, H., et al.: An annotation-free restoration network for cataractous fundus images. TMI 41, 1699\u20131710 (2022)","journal-title":"TMI"},{"key":"8_CR11","unstructured":"World Health Organization, et al.: World report on vision (2019)"},{"issue":"4","key":"8_CR12","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1007\/s11263-019-01283-0","volume":"128","author":"J Park","year":"2020","unstructured":"Park, J., Woo, S., Lee, J.Y., Kweon, I.S.: A simple and light-weight attention module for convolutional neural networks. IJCV 128(4), 783\u2013798 (2020)","journal-title":"IJCV"},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.cmpb.2019.06.016","volume":"178","author":"O Perdomo","year":"2019","unstructured":"Perdomo, O., et al.: Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography. Comput. Methods Program. Biomed. 178, 181\u2013189 (2019)","journal-title":"Comput. Methods Program. Biomed."},{"key":"8_CR14","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.ins.2018.01.051","volume":"441","author":"U Raghavendra","year":"2018","unstructured":"Raghavendra, U., Fujita, H., Bhandary, S.V., Gudigar, A., Tan, J.H., Acharya, U.R.: Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Inf. Sci. 441, 41\u201349 (2018)","journal-title":"Inf. Sci."},{"key":"8_CR15","doi-asserted-by":"publisher","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-net: efficient channel attention for deep convolutional neural networks. In: CVPR, pp. 11531\u201311539 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01155","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"8_CR16","doi-asserted-by":"publisher","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00813","DOI":"10.1109\/CVPR.2018.00813"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: ECCV, pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"2","key":"8_CR18","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1109\/JBHI.2019.2914690","volume":"24","author":"X Xu","year":"2020","unstructured":"Xu, X., Zhang, L., Li, J., Guan, Y., Zhang, L.: A hybrid global-local representation CNN model for automatic cataract grading. IEEE J. Biomed. Health Inform. 24(2), 556\u2013567 (2020). https:\/\/doi.org\/10.1109\/JBHI.2019.2914690","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"8_CR19","unstructured":"Yang, J., et al.: MedMNIST v2: a large-scale lightweight benchmark for 2D and 3D biomedical image classification. arXiv preprint arXiv:2110.14795 (2021)"},{"issue":"3","key":"8_CR20","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/s11633-022-1329-0","volume":"19","author":"XQ Zhang","year":"2022","unstructured":"Zhang, X.Q., Hu, Y., Xiao, Z.J., Fang, J.S., Higashita, R., Liu, J.: Machine learning for cataract classification\/grading on ophthalmic imaging modalities: a survey. Mach. Intell. Res. 19(3), 184\u2013208 (2022)","journal-title":"Mach. Intell. Res."},{"key":"8_CR21","doi-asserted-by":"publisher","first-page":"102499","DOI":"10.1016\/j.media.2022.102499","volume":"80","author":"X Zhang","year":"2022","unstructured":"Zhang, X., et al.: Attention to region: region-based integration-and-recalibration networks for nuclear cataract classification using AS-OCT images. Med. Image Anal. 80, 102499 (2022)","journal-title":"Med. Image Anal."},{"key":"8_CR22","doi-asserted-by":"publisher","unstructured":"Zhang, X., et al.: Adaptive feature squeeze network for nuclear cataract classification in AS-OCT image. JBI 128, 104037 (2022). https:\/\/doi.org\/10.1016\/j.jbi.2022.104037, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1532046422000533","DOI":"10.1016\/j.jbi.2022.104037"},{"key":"8_CR23","first-page":"109109","volume":"250","author":"X Zhang","year":"2022","unstructured":"Zhang, X., et al.: CCA-net: clinical-awareness attention network for nuclear cataract classification in AS-OCT. KBS 250, 109109 (2022)","journal-title":"KBS"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44216-2_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T07:04:43Z","timestamp":1695279883000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44216-2_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442155","9783031442162"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44216-2_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"4","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)"}},{"value":"type of other papers accepted  : 9 Abstract","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}