{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:25:29Z","timestamp":1772907929120,"version":"3.50.1"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164514","type":"print"},{"value":"9783031164521","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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16452-1_9","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T21:25:46Z","timestamp":1663277146000},"page":"88-98","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Uni4Eye: Unified 2D and\u00a03D Self-supervised Pre-training via\u00a0Masked Image Modeling Transformer for\u00a0Ophthalmic Image Classification"],"prefix":"10.1007","author":[{"given":"Zhiyuan","family":"Cai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaqing","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoying","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"9_CR1","unstructured":"Atito, S., Awais, M., Kittler, J.: Sit: self-supervised vision transformer. arXiv preprint arXiv: 2104.03602 (2021)"},{"key":"9_CR2","unstructured":"Bao, H., Dong, L., et al.: BEIT: BERT pre-training of image transformers. In: International Conference on Learning Representations, ICLR (2022)"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Cai, Z., Lin, L., He, H., Tang, X.: Corolla: an efficient multi-modality fusion framework with supervised contrastive learning for glaucoma grading. arXiv preprint arXiv: 2201.03795 (2022)","DOI":"10.1109\/ISBI52829.2022.9761712"},{"key":"9_CR4","unstructured":"Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations. In: Advances in Neural Information Processing Systems, NeurIPS, vol. 33 (2020)"},{"key":"9_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101539","volume":"58","author":"L Chen","year":"2019","unstructured":"Chen, L., Bentley, P., et al.: Self-supervised learning for medical image analysis using image context restoration. IEEE Trans. Med. Imaging 58, 101539 (2019). https:\/\/doi.org\/10.1016\/j.media.2019.101539","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR6","unstructured":"Chen, S., Ma, K., et al.: Med3D: transfer learning for 3D medical image analysis. arXiv preprint arXiv: 1904.00625 (2019)"},{"key":"9_CR7","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. arXiv preprint arXiv: 2002.05709 (2020)"},{"key":"9_CR8","unstructured":"Cordeiro, F.R., Sachdeva, R., et al.: LongReMix: robust learning with high confidence samples in a noisy label environment. arXiv preprint arXiv: 2103.04173 (2021)"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"9_CR10","unstructured":"Donahue, J., Simonyan, K.: Large scale adversarial representation learning. In: Advances in Neural Information Processing Systems, NeurIPS, vol. 32 (2019)"},{"key":"9_CR11","unstructured":"Dosovitskiy, A., Beyer, L., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv: 2010.11929 (2021)"},{"key":"9_CR12","unstructured":"Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728 (2018)"},{"key":"9_CR13","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, NeurIPS, vol. 27 (2014)"},{"key":"9_CR14","unstructured":"He, H., Lin, L., Cai, Z., Tang, X.: JOINED: prior guided multi-task learning for joint optic disc\/cup segmentation and fovea detection. In: International Conference on Medical Imaging with Deep Learning, MIDL (2022)"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. arXiv preprint arXiv: 2111.06377 (2021)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"9_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/978-3-030-87196-3_11","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Huang","year":"2021","unstructured":"Huang, Y., Lin, L., Cheng, P., Lyu, J., Tang, X.: Lesion-based contrastive learning for diabetic retinopathy grading from fundus images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 113\u2013123. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_11"},{"issue":"2","key":"9_CR18","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1109\/4.996","volume":"23","author":"N Kanopoulos","year":"1988","unstructured":"Kanopoulos, N., Vasanthavada, N., Baker, R.L.: Design of an image edge detection filter using the Sobel operator. IEEE J. Solid State Circuits 23(2), 358\u2013367 (1988)","journal-title":"IEEE J. Solid State Circuits"},{"issue":"9","key":"9_CR19","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.1109\/TMI.2021.3075244","volume":"40","author":"X Li","year":"2021","unstructured":"Li, X., Hu, X., et al.: Rotation-oriented collaborative self-supervised learning for retinal disease diagnosis. IEEE Trans. Med. Imaging 40(9), 2284\u20132294 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"9_CR20","doi-asserted-by":"publisher","first-page":"4023","DOI":"10.1109\/TMI.2020.3008871","volume":"39","author":"X Li","year":"2020","unstructured":"Li, X., Jia, M., Islam, M.T., Yu, L., Xing, L.: Self-supervised feature learning via exploiting multi-modal data for retinal disease diagnosis. IEEE Trans. Med. Imaging 39(12), 4023\u20134033 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"9_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-020-00755-0","volume":"7","author":"L Lin","year":"2020","unstructured":"Lin, L., et al.: The SUSTech-SYSU dataset for automated exudate detection and diabetic retinopathy grading. Sci. Data 7(1), 1\u201310 (2020)","journal-title":"Sci. Data"},{"key":"9_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/978-3-030-87237-3_7","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"L Lin","year":"2021","unstructured":"Lin, L., et al.: BSDA-Net: a boundary shape and distance aware joint learning framework for segmenting and classifying OCTA images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 65\u201375. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_7"},{"key":"9_CR23","unstructured":"Loshchilov, I., Hutter, F.: Fixing weight decay regularization in adam. arXiv preprint arXiv: 1711.05101 (2017)"},{"key":"9_CR24","unstructured":"Oliver, A., Odena, A., Raffel, C., Cubuk, E.D., Goodfellow, I.J.: Realistic evaluation of deep semi-supervised learning algorithms. In: Advances in Neural Information Processing Systems, NeurIPS, vol. 31 (2019)"},{"key":"9_CR25","unstructured":"Paszke, A., Gross, S., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, NeurIPS, vol. 32 (2019)"},{"key":"9_CR26","unstructured":"Taleb, A., Loetzsch, W., et al.: 3D self-supervised methods for medical imaging. In: Advances in Neural Information Processing Systems, NeurIPS, vol. 33 (2020)"},{"key":"9_CR27","unstructured":"Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, ICML, pp. 6105\u20136114 (2019)"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Wei, C., Fan, H., Xie, S., Wu, C.Y., Yuille, A., Feichtenhofer, C.: Masked feature prediction for self-supervised visual pre-training. arXiv preprint arXiv: 2112.09133 (2021)","DOI":"10.1109\/CVPR52688.2022.01426"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Ye, M., Zhang, X., Yuen, P.C., Chang, S.F.: Unsupervised embedding learning via invariant and spreading instance feature. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp. 6210\u20136219 (2019)","DOI":"10.1109\/CVPR.2019.00637"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Zhou, H.Y., Lu, C., et al.: Preservational learning improves self-supervised medical image models by reconstructing diverse contexts. In: The IEEE International Conference on Computer Vision, ICCV, pp. 3499\u20133509 (2021)","DOI":"10.1109\/ICCV48922.2021.00348"},{"key":"9_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/978-3-030-32251-9_46","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"X Zhuang","year":"2019","unstructured":"Zhuang, X., Li, Y., Hu, Y., Ma, K., Yang, Y., Zheng, Y.: Self-supervised feature learning for 3D medical images by playing a Rubik\u2019s cube. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 420\u2013428. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_46"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16452-1_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T11:43:07Z","timestamp":1710243787000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16452-1_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164514","9783031164521"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16452-1_9","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":"16 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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)"}}]}}