{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T16:18:33Z","timestamp":1771949913172,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030872366","type":"print"},{"value":"9783030872373","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87237-3_6","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:19:41Z","timestamp":1632377981000},"page":"55-64","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Local-Global Dual Perception Based Deep Multiple Instance Learning for Retinal Disease Classification"],"prefix":"10.1007","author":[{"given":"Qi","family":"Bi","sequence":"first","affiliation":[]},{"given":"Shuang","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Cheng","family":"Bian","sequence":"additional","affiliation":[]},{"given":"Lijun","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Hanruo","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Yefeng","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"issue":"9","key":"6_CR1","doi-asserted-by":"publisher","first-page":"e888","DOI":"10.1016\/S2214-109X(17)30293-0","volume":"5","author":"R Bourne","year":"2017","unstructured":"Bourne, 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":"6_CR2","doi-asserted-by":"crossref","unstructured":"Ting, D., et al.: Deep learning in ophthalmology: the technical and clinical considerations. Prog. Retinal Eye Res. 72, 100759 (2019)","DOI":"10.1016\/j.preteyeres.2019.04.003"},{"key":"6_CR3","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1109\/RBME.2010.2084567","volume":"3","author":"M Abr\u00e0moff","year":"2010","unstructured":"Abr\u00e0moff, M., Garvin, M., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169\u2013208 (2010)","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"3","key":"6_CR4","doi-asserted-by":"publisher","first-page":"556","DOI":"10.2337\/dc11-1909","volume":"35","author":"J Yau","year":"2012","unstructured":"Yau, J., et al.: Global prevalence and major risk factors of diabetic retinopathy. Diab. Care 35(3), 556\u2013564 (2012)","journal-title":"Diab. Care"},{"issue":"11","key":"6_CR5","doi-asserted-by":"publisher","first-page":"2081","DOI":"10.1016\/j.ophtha.2014.05.013","volume":"121","author":"Y Tham","year":"2014","unstructured":"Tham, Y., Li, X., Wong, T., Quigley, H., Aung, T., Cheng, C.: Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 121(11), 2081\u20132090 (2014)","journal-title":"Ophthalmology"},{"issue":"2","key":"6_CR6","doi-asserted-by":"publisher","first-page":"e106","DOI":"10.1016\/S2214-109X(13)70145-1","volume":"2","author":"W Wong","year":"2014","unstructured":"Wong, W., et al.: Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob. Health 2(2), e106\u2013e116 (2014)","journal-title":"Lancet Glob. Health"},{"issue":"22","key":"6_CR7","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. J. Am. Med. Assoc. 316(22), 2402\u20132410 (2016)","journal-title":"J. Am. Med. Assoc."},{"issue":"22","key":"6_CR8","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1001\/jama.2017.18152","volume":"318","author":"D Ting","year":"2017","unstructured":"Ting, D., et al.: Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. J. Am. Med. Assoc. 318(22), 2211\u20132223 (2017)","journal-title":"J. Am. Med. Assoc."},{"issue":"12","key":"6_CR9","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1016\/j.ophtha.2019.07.024","volume":"126","author":"S Phene","year":"2019","unstructured":"Phene, S., et al.: Deep learning and glaucoma specialists: the relative importance of optic disc features to predict glaucoma referral in fundus photographs. Ophthalmology 126(12), 1627\u20131639 (2019)","journal-title":"Ophthalmology"},{"issue":"12","key":"6_CR10","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1001\/jamaophthalmol.2019.3501","volume":"137","author":"H Liu","year":"2019","unstructured":"Liu, H., et al.: Development and validation of a deep learning system to detect glaucomatous optic neuropathy using fundus photographs. JAMA Ophthalmol. 137(12), 1353\u20131360 (2019)","journal-title":"JAMA Ophthalmol."},{"issue":"11","key":"6_CR11","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1001\/jamaophthalmol.2017.3782","volume":"135","author":"P Burlina","year":"2017","unstructured":"Burlina, P., Joshi, N., Pekala, M., Pacheco, K., Freund, D., Bressler, N.: Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 135(11), 1170\u20131176 (2017)","journal-title":"JAMA Ophthalmol."},{"issue":"6","key":"6_CR12","doi-asserted-by":"publisher","first-page":"1410","DOI":"10.1016\/j.ophtha.2018.02.037","volume":"125","author":"F Grassmann","year":"2018","unstructured":"Grassmann, F., et al.: A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology 125(6), 1410\u20131420 (2018)","journal-title":"Ophthalmology"},{"key":"6_CR13","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representation (2015)"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"6_CR15","unstructured":"Zhang, M., Li, J., Ji, W., Piao, Y., Lu, H.: Memory-oriented decoder for light field salient object detection. In: Advances in Neural Information Processing Systems, pp. 898\u2013908 (2019)"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Ji, W., et al.: Learning calibrated medical image segmentation via multi-rater agreement modeling. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 12341\u201312351 (2021)","DOI":"10.1109\/CVPR46437.2021.01216"},{"key":"6_CR17","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, vol. 80, pp. 2127\u20132136 (2018)"},{"key":"6_CR18","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1023\/A:1011139631724","volume":"42","author":"A Oliva","year":"2001","unstructured":"Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vision 42, 145\u2013175 (2001)","journal-title":"Int. J. Comput. Vision"},{"issue":"1","key":"6_CR19","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/S0004-3702(96)00034-3","volume":"89","author":"T Dietterich","year":"1997","unstructured":"Dietterich, T., Lathrop, R., Lozano-P\u00e9rez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1), 31\u201371 (1997)","journal-title":"Artif. Intell."},{"issue":"1","key":"6_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1023\/B:NEPL.0000016836.03614.9f","volume":"19","author":"M Zhang","year":"2004","unstructured":"Zhang, M., Zhou, Z.: Improve multi-instance neural networks through feature selection. Neural Process. Lett. 19(1), 1\u201310 (2004)","journal-title":"Neural Process. Lett."},{"key":"6_CR21","unstructured":"Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Advances in Neural Information Processing Systems (2003)"},{"key":"6_CR22","doi-asserted-by":"publisher","first-page":"4911","DOI":"10.1109\/TIP.2020.2975718","volume":"29","author":"Q Bi","year":"2020","unstructured":"Bi, Q., Qin, K., Zhang, H., Li, Z., Xu, K., Xia, G.: A multiple-instance densely-connected ConvNet for aerial scene classification. IEEE Trans. Image Process. 29, 4911\u20134926 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"6_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1007\/978-3-030-32251-9_58","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Li","year":"2019","unstructured":"Li, S., et al.: Multi-instance multi-scale CNN for medical image classification. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 531\u2013539. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_58"},{"key":"6_CR24","unstructured":"APTOS 2019 Blindness Detection (2019). https:\/\/www.kaggle.com\/c\/aptos2019-blindness-detection\/data"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Li, L., Xu, M., Wang, X., Jiang, L., Liu, H.: Attention based glaucoma detection: a large-scale database and CNN model. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 10571\u201310580 (2019)","DOI":"10.1109\/CVPR.2019.01082"},{"key":"6_CR26","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.patcog.2017.08.026","volume":"74","author":"X Wang","year":"2016","unstructured":"Wang, X., Yan, Y., Peng, T., Xiang, B., Liu, W.: Revisiting multiple instance neural networks. Pattern Recogn. 74, 15\u201324 (2016)","journal-title":"Pattern Recogn."},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87237-3_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:36:58Z","timestamp":1673311018000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87237-3_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872366","9783030872373"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87237-3_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually.","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)"}}]}}