{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T07:06:46Z","timestamp":1747120006543,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031165245"},{"type":"electronic","value":"9783031165252"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16525-2_13","type":"book-chapter","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T23:03:00Z","timestamp":1663196580000},"page":"125-134","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Tiny-Lesion Segmentation in\u00a0OCT via\u00a0Multi-scale Wavelet Enhanced Transformer"],"prefix":"10.1007","author":[{"given":"Meng","family":"Wang","sequence":"first","affiliation":[]},{"given":"Kai","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Xinxing","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yuanyuan","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Yanyu","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Rick Siow Mong","family":"Goh","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Huazhu","family":"Fu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Abdulrahman, A.A., Rasheed, M., Shihab, S.: The analytic of image processing smoothing spaces using wavelet. In: Journal of Physics: Conference Series, vol. 1879, p. 022118. IOP Publishing (2021)","key":"13_CR1","DOI":"10.1088\/1742-6596\/1879\/2\/022118"},{"unstructured":"Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. CoRR abs\/2102.04306 (2021). https:\/\/arxiv.org\/abs\/2102.04306","key":"13_CR2"},{"issue":"8","key":"13_CR3","doi-asserted-by":"publisher","first-page":"135","DOI":"10.3390\/electronics7080135","volume":"7","author":"N Chervyakov","year":"2018","unstructured":"Chervyakov, N., Lyakhov, P., Kaplun, D., Butusov, D., Nagornov, N.: Analysis of the quantization noise in discrete wavelet transform filters for image processing. Electronics 7(8), 135 (2018)","journal-title":"Electronics"},{"unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)","key":"13_CR4"},{"issue":"10","key":"13_CR5","doi-asserted-by":"publisher","first-page":"3008","DOI":"10.1109\/TMI.2020.2983721","volume":"39","author":"S Feng","year":"2020","unstructured":"Feng, S., Zhao, H., Shi, F., Cheng, X., Wang, M., Ma, Y., Xiang, D., Zhu, W., Chen, X.: Cpfnet: context pyramid fusion network for medical image segmentation. IEEE Trans. Med. imaging 39(10), 3008\u20133018 (2020)","journal-title":"IEEE Trans. Med. imaging"},{"unstructured":"Fujieda, S., Takayama, K., Hachisuka, T.: Wavelet convolutional neural networks. arXiv preprint arXiv:1805.08620 (2018)","key":"13_CR6"},{"key":"13_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/978-3-030-87199-4_6","volume-title":"Medical Image Computing and Computer Assisted Intervention","author":"Y Gao","year":"2021","unstructured":"Gao, Y., Zhou, M., Metaxas, D.N.: UTNet: a hybrid transformer architecture for medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 61\u201371. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_6"},{"issue":"10","key":"13_CR8","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu, Z., et al.: Ce-net: context encoder network for 2d medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281\u20132292 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3354","key":"13_CR9","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1126\/science.129.3354.962","volume":"129","author":"LD Harmon","year":"1959","unstructured":"Harmon, L.D.: Artificial neuron. Science 129(3354), 962\u2013963 (1959)","journal-title":"Science"},{"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)","key":"13_CR10","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"crossref","unstructured":"Huang, D., et al.: Optical coherence tomography. Sci. (Am. Assoc. Adv. Sci) 254(5035), 1178\u20131181 (1991)","key":"13_CR11","DOI":"10.1126\/science.1957169"},{"doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. CoRR abs\/2103.14030 (2021), https:\/\/arxiv.org\/abs\/2103.14030","key":"13_CR12","DOI":"10.1109\/ICCV48922.2021.00986"},{"unstructured":"Oktay, O., et al.: Attention u-net: learning where to look for the pancreas (2018)","key":"13_CR13"},{"doi-asserted-by":"crossref","unstructured":"Oyallon, E., Belilovsky, E., Zagoruyko, S.: Scaling the scattering transform: deep hybrid networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5618\u20135627 (2017)","key":"13_CR14","DOI":"10.1109\/ICCV.2017.599"},{"unstructured":"Rodriguez, M.X.B., et al.: Deep adaptive wavelet network. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3111\u20133119 (2020)","key":"13_CR15"},{"key":"13_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"9","key":"13_CR17","first-page":"01","volume":"9","author":"N Sathiyanathan","year":"2018","unstructured":"Sathiyanathan, N.: Medical image compression using view compensated wavelet transform. J. Glob. Res. Comput. Sci. 9(9), 01\u201304 (2018)","journal-title":"J. Glob. Res. Comput. Sci."},{"unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)","key":"13_CR18"},{"issue":"2","key":"13_CR19","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/TMI.2021.3112716","volume":"41","author":"M Wang","year":"2021","unstructured":"Wang, M., et al.: Mstganet: automatic drusen segmentation from retinal oct images. IEEE Trans. Med. Imaging 41(2), 394\u2013406 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"13_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested u-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"}],"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\/978-3-031-16525-2_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T08:08:08Z","timestamp":1671610088000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16525-2_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031165245","9783031165252"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16525-2_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 September 2022","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":"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":"22 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":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"omia2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/omia9\/home","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":"33","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":"20","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":"61% - 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)"}}]}}