{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T16:53:01Z","timestamp":1768927981931,"version":"3.49.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439896","type":"print"},{"value":"9783031439902","type":"electronic"}],"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\/978-3-031-43990-2_60","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:07:48Z","timestamp":1696115268000},"page":"639-648","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Fundus-Enhanced Disease-Aware Distillation Model for\u00a0Retinal Disease Classification from\u00a0OCT Images"],"prefix":"10.1007","author":[{"given":"Lehan","family":"Wang","sequence":"first","affiliation":[]},{"given":"Weihang","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Mei","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Chubin","family":"Ou","sequence":"additional","affiliation":[]},{"given":"Xiaomeng","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"60_CR1","doi-asserted-by":"crossref","unstructured":"Chen, D., Mei, J.P., Zhang, H., Wang, C., Feng, Y., Chen, C.: Knowledge distillation with the reused teacher classifier. In: CVPR, pp. 11933\u201311942 (2022)","DOI":"10.1109\/CVPR52688.2022.01163"},{"key":"60_CR2","volume-title":"The Retina Illustrated","author":"J Ehlers","year":"2019","unstructured":"Ehlers, J.: The Retina Illustrated. Thieme Medical Publishers, Incorporated (2019)"},{"issue":"8","key":"60_CR3","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1109\/TMI.2019.2898414","volume":"38","author":"L Fang","year":"2019","unstructured":"Fang, L., Wang, C., Li, S., Rabbani, H., Chen, X., Liu, Z.: Attention to lesion: lesion-aware convolutional neural network for retinal optical coherence tomography image classification. IEEE Trans. Med. Imaging 38(8), 1959\u20131970 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"60_CR4","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1109\/TMI.2021.3059956","volume":"40","author":"X He","year":"2021","unstructured":"He, X., Deng, Y., Fang, L., Peng, Q.: Multi-modal retinal image classification with modality-specific attention network. IEEE Trans. Med. Imaging 40, 1591\u20131602 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"60_CR5","unstructured":"Hinton, G., Vinyals, O., Dean, J., et al.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.025312(7) (2015)"},{"issue":"5035","key":"60_CR6","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1126\/science.1957169","volume":"254","author":"D Huang","year":"1991","unstructured":"Huang, D., et al.: Optical coherence tomography. Science 254(5035), 1178\u20131181 (1991)","journal-title":"Science"},{"issue":"7","key":"60_CR7","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1109\/LSP.2019.2917779","volume":"26","author":"L Huang","year":"2019","unstructured":"Huang, L., He, X., Fang, L., Rabbani, H., Chen, X.: Automatic classification of retinal optical coherence tomography images with layer guided convolutional neural network. IEEE Signal Process. Lett. 26(7), 1026\u20131030 (2019)","journal-title":"IEEE Signal Process. Lett."},{"issue":"2","key":"60_CR8","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1364\/BOE.8.000579","volume":"8","author":"SPK Karri","year":"2017","unstructured":"Karri, S.P.K., Chakraborty, D., Chatterjee, J.: Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. Biomed. Opt. Express 8(2), 579\u2013592 (2017)","journal-title":"Biomed. Opt. Express"},{"issue":"5","key":"60_CR9","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":"4","key":"60_CR10","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.oret.2016.12.009","volume":"1","author":"CS Lee","year":"2017","unstructured":"Lee, C.S., Baughman, D.M., Lee, A.Y.: Deep learning is effective for classifying normal versus age-related macular degeneration oct images. Ophthalmol. Retina 1(4), 322\u2013327 (2017)","journal-title":"Ophthalmol. Retina"},{"key":"60_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/3298606","volume":"2016","author":"G Lema\u00eetre","year":"2016","unstructured":"Lema\u00eetre, G., et al.: Classification of SD-OCT volumes using local binary patterns: experimental validation for DME detection. J. Ophthalmol. 2016, 1\u201314 (2016). https:\/\/doi.org\/10.1155\/2016\/3298606","journal-title":"J. Ophthalmol."},{"key":"60_CR12","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: Multi-modal multi-instance learning for retinal disease recognition. In: ACMMM, pp. 2474\u20132482 (2021)","DOI":"10.1145\/3474085.3475418"},{"key":"60_CR13","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.neucom.2019.08.079","volume":"369","author":"X Li","year":"2019","unstructured":"Li, X., Shen, L., Shen, M., Tan, F., Qiu, C.S.: Deep learning based early stage diabetic retinopathy detection using optical coherence tomography. Neurocomputing 369, 134\u2013144 (2019)","journal-title":"Neurocomputing"},{"key":"60_CR14","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-031-16525-2_6","volume-title":"Ophthalmic Medical Image Analysis: 9th International Workshop, OMIA 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings","author":"Y Li","year":"2022","unstructured":"Li, Y., et al.: Multimodal information fusion for\u00a0glaucoma and\u00a0diabetic retinopathy classification. In: Antony, B., Fu, H., Lee, C.S., MacGillivray, T., Xu, Y., Zheng, Y. (eds.) Ophthalmic Medical Image Analysis: 9th International Workshop, OMIA 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings, pp. 53\u201362. Springer International Publishing, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16525-2_6"},{"key":"60_CR15","doi-asserted-by":"crossref","unstructured":"Liu, X., Bai, Y., Jiang, M.: One-stage attention-based network for image classification and segmentation on optical coherence tomography image. In: SMC, pp. 3025\u20133029. IEEE (2021)","DOI":"10.1109\/SMC52423.2021.9658976"},{"issue":"5","key":"60_CR16","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1016\/j.media.2011.06.005","volume":"15","author":"YY Liu","year":"2011","unstructured":"Liu, Y.Y., Chen, M., Ishikawa, H., Wollstein, G., Schuman, J.S., Rehg, J.M.: Automated macular pathology diagnosis in retinal oct images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding. Med. Image Anal. 15(5), 748\u2013759 (2011)","journal-title":"Med. Image Anal."},{"key":"60_CR17","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-3-030-16638-0_4","volume-title":"High Resolution Imaging in Microscopy and Ophthalmology","author":"PL M\u00fcller","year":"2019","unstructured":"M\u00fcller, P.L., Wolf, S., Dolz-Marco, R., Tafreshi, A., Schmitz-Valckenberg, S., Holz, F.G.: Ophthalmic Diagnostic Imaging: Retina. In: Bille, J.F. (ed.) High Resolution Imaging in Microscopy and Ophthalmology, pp. 87\u2013106. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-16638-0_4"},{"issue":"9","key":"60_CR18","doi-asserted-by":"publisher","first-page":"210","DOI":"10.23919\/JCC.2021.09.016","volume":"18","author":"Z Ou","year":"2021","unstructured":"Ou, Z., et al.: M 2 LC-Net: A multi-modal multi-disease long-tailed classification network for real clinical scenes. China Commun.D 18(9), 210\u2013220 (2021)","journal-title":"China Commun.D"},{"issue":"2","key":"60_CR19","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/data6020014","volume":"6","author":"S Pachade","year":"2021","unstructured":"Pachade, S., et al.: Retinal fundus multi-disease image dataset (RFMid): a dataset for multi-disease detection research. Data 6(2), 14 (2021)","journal-title":"Data"},{"key":"60_CR20","doi-asserted-by":"crossref","unstructured":"Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3967\u20133976 (2019)","DOI":"10.1109\/CVPR.2019.00409"},{"key":"60_CR21","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)"},{"key":"60_CR22","doi-asserted-by":"crossref","unstructured":"Shu, C., Liu, Y., Gao, J., Yan, Z., Shen, C.: Channel-wise knowledge distillation for dense prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5311\u20135320 (2021)","DOI":"10.1109\/ICCV48922.2021.00526"},{"issue":"10","key":"60_CR23","doi-asserted-by":"publisher","first-page":"3568","DOI":"10.1364\/BOE.5.003568","volume":"5","author":"PP Srinivasan","year":"2014","unstructured":"Srinivasan, P.P., et al.: Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed. Opt. Express 5(10), 3568\u20133577 (2014)","journal-title":"Biomed. Opt. Express"},{"key":"60_CR24","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Learning two-stream CNN for multi-modal age-related macular degeneration categorization. IEEE J. Biomed. Health Inform. 26(8), 4111-4122 (2022)","DOI":"10.1109\/JBHI.2022.3171523"},{"key":"60_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1007\/978-3-030-32239-7_18","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"W Wang","year":"2019","unstructured":"Wang, W., et al.: Two-stream CNN with loose pair training for multi-modal AMD categorization. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 156\u2013164. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_18"},{"issue":"3","key":"60_CR26","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/s11517-018-1915-z","volume":"57","author":"TK Yoo","year":"2019","unstructured":"Yoo, T.K., Choi, J.Y., Seo, J.G., Ramasubramanian, B., Selvaperumal, S., Kim, D.W.: The possibility of the combination of oct and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment. Med. Biol. Eng. Comput. 57(3), 677\u2013687 (2019)","journal-title":"Med. Biol. Eng. Comput."},{"key":"60_CR27","doi-asserted-by":"crossref","unstructured":"Zhao, B., Cui, Q., Song, R., Qiu, Y., Liang, J.: Decoupled knowledge distillation. In: CVPR, pp. 11953\u201311962 (2022)","DOI":"10.1109\/CVPR52688.2022.01165"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43990-2_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T15:43:01Z","timestamp":1710171781000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43990-2_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439896","9783031439902"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43990-2_60","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","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":"8 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":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","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","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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":"5","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)"}}]}}