{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:17:06Z","timestamp":1742930226667,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031440120"},{"type":"electronic","value":"9783031440137"}],"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\/978-3-031-44013-7_11","type":"book-chapter","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T23:02:39Z","timestamp":1694818959000},"page":"102-111","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-Modality Grading of\u00a0Keratoconus Severity Based on\u00a0Corneal Topography and\u00a0Clinical Indicators"],"prefix":"10.1007","author":[{"given":"Xin","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjie","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weifang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Mateen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinjian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,16]]},"reference":[{"issue":"5","key":"11_CR1","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1007\/s12559-021-09880-3","volume":"14","author":"AH Al-Timemy","year":"2022","unstructured":"Al-Timemy, A.H., Ghaeb, N.H., Mosa, Z.M., Escudero, J.: Deep transfer learning for improved detection of keratoconus using corneal topographic maps. Cogn. Comput. 14(5), 1627\u20131642 (2022)","journal-title":"Cogn. Comput."},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"issue":"10","key":"11_CR3","doi-asserted-by":"publisher","first-page":"3898","DOI":"10.1109\/JBHI.2021.3079430","volume":"25","author":"R Feng","year":"2021","unstructured":"Feng, R., et al.: Kernet: a novel deep learning approach for keratoconus and sub-clinical keratoconus detection based on raw data of the pentacam hr system. IEEE J. Biomed. Health Inform. 25(10), 3898\u20133910 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Annals of statistics, pp. 1189\u20131232 (2001)","DOI":"10.1214\/aos\/1013203451"},{"issue":"2","key":"11_CR5","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1364\/BOE.480564","volume":"14","author":"S Gao","year":"2023","unstructured":"Gao, S., et al.: Lkg-net: lightweight keratoconus grading network based on corneal topography. Biomed. Opt. Express 14(2), 799\u2013814 (2023)","journal-title":"Biomed. Opt. Express"},{"key":"11_CR6","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":"11_CR7","doi-asserted-by":"crossref","unstructured":"Hosmer Jr, D.W., Lemeshow, S., Sturdivant, R.X.: Applied logistic regression, vol. 398. John Wiley & Sons (2013)","DOI":"10.1002\/9781118548387"},{"key":"11_CR8","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"},{"key":"11_CR9","unstructured":"Huo, X., et al.: Hifuse: hierarchical multi-scale feature fusion network for medical image classification. arXiv preprint arXiv:2209.10218 (2022)"},{"key":"11_CR10","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456. PMLR (2015)"},{"key":"11_CR11","unstructured":"Ke, G., et al.: Lightgbm: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems 30 (2017)"},{"issue":"2","key":"11_CR12","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1167\/tvst.9.2.53","volume":"9","author":"BI Kuo","year":"2020","unstructured":"Kuo, B.I., et al.: Keratoconus screening based on deep learning approach of corneal topography. Translational Vision Sci. Technol. 9(2), 53\u201353 (2020)","journal-title":"Translational Vision Sci. Technol."},{"key":"11_CR13","doi-asserted-by":"publisher","first-page":"84344","DOI":"10.1109\/ACCESS.2021.3086021","volume":"9","author":"A Lavric","year":"2021","unstructured":"Lavric, A., et al.: Keratoconus severity detection from elevation, topography and pachymetry raw data using a machine learning approach. IEEE Access 9, 84344\u201384355 (2021)","journal-title":"IEEE Access"},{"issue":"1","key":"11_CR14","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.irbm.2020.12.002","volume":"43","author":"T Liu","year":"2022","unstructured":"Liu, T., Huang, J., Liao, T., Pu, R., Liu, S., Peng, Y.: A hybrid deep learning model for predicting molecular subtypes of human breast cancer using multimodal data. IRBM 43(1), 62\u201374 (2022)","journal-title":"IRBM"},{"key":"11_CR15","unstructured":"Mehta, S., Rastegari, M.: Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv preprint arXiv:2110.02178 (2021)"},{"key":"11_CR16","unstructured":"Mitchell, T.M., Mitchell, T.M.: Machine learning, vol. 1. McGraw-hill New York (1997)"},{"key":"11_CR17","unstructured":"Platt, J.: Sequential minimal optimization: a fast algorithm for training support vector machines (1998)"},{"issue":"6","key":"11_CR18","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/h0042519","volume":"65","author":"F Rosenblatt","year":"1958","unstructured":"Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386 (1958)","journal-title":"Psychol. Rev."},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Salzberg, S.L.: C4. 5: programs for machine learning by j. ross quinlan. Morgan Kaufmann Publishers, Inc., 1993 (1994)","DOI":"10.1007\/BF00993309"},{"key":"11_CR20","volume":"15","author":"T Sarkar","year":"2022","unstructured":"Sarkar, T.: Xbnet: an extremely boosted neural network. Intelligent Syst. Appli. 15, 200097 (2022)","journal-title":"Intelligent Syst. Appli."},{"issue":"1","key":"11_CR21","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1159\/000357979","volume":"232","author":"N Sorkin","year":"2014","unstructured":"Sorkin, N., Varssano, D.: Corneal collagen crosslinking: a systematic review. Ophthalmologica 232(1), 10\u201327 (2014)","journal-title":"Ophthalmologica"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Srinivas, A., Lin, T.Y., Parmar, N., Shlens, J., Abbeel, P., Vaswani, A.: Bottleneck transformers for visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16519\u201316529 (2021)","DOI":"10.1109\/CVPR46437.2021.01625"},{"key":"11_CR23","unstructured":"Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"11_CR24","doi-asserted-by":"publisher","unstructured":"Vanathi, M., Sidhu, N.: Classifications and patterns of keratoconus. In: Keratoconus: Diagnosis and Treatment, pp. 59\u201367. Springer (2022). https:\/\/doi.org\/10.1007\/978-981-19-3571-8_18","DOI":"10.1007\/978-981-19-3571-8_18"},{"key":"11_CR25","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)"},{"key":"11_CR26","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":"11_CR27","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"issue":"1","key":"11_CR28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-020-01362-0","volume":"21","author":"R Yan","year":"2021","unstructured":"Yan, R., et al.: Richer fusion network for breast cancer classification based on multimodal data. BMC Med. Inform. Decis. Mak. 21(1), 1\u201315 (2021)","journal-title":"BMC Med. Inform. Decis. Mak."}],"container-title":["Lecture Notes in Computer Science","Ophthalmic Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44013-7_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T23:12:28Z","timestamp":1695078748000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44013-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031440120","9783031440137"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44013-7_11","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":"16 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"OMIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Ophthalmic Medical Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"12 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"omia2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/omiax\/","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":"CMT System","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27","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":"16","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":"59% - 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","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","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}