{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T09:42:37Z","timestamp":1743154957160,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031336577"},{"type":"electronic","value":"9783031336584"}],"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\/978-3-031-33658-4_15","type":"book-chapter","created":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T08:02:15Z","timestamp":1685347335000},"page":"161-169","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data Augmentation by\u00a0Fourier Transformation for Class-Imbalance: Application to\u00a0Medical Image Quality Assessment"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8342-0507","authenticated-orcid":false,"given":"Zhicheng","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0231-1390","authenticated-orcid":false,"given":"Yanbin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3916-8105","authenticated-orcid":false,"given":"Xuru","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8602-6380","authenticated-orcid":false,"given":"Liqin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"issue":"4","key":"15_CR1","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.oret.2018.11.008","volume":"3","author":"JF Russell","year":"2019","unstructured":"Russell, J.F., et al.: Longitudinal wide-field swept-source OCT angiography of neovascularization in proliferative diabetic retinopathy after panretinal photocoagulation. Ophthalmol. Retina 3(4), 350\u2013361 (2019)","journal-title":"Ophthalmol. Retina"},{"issue":"8","key":"15_CR2","doi-asserted-by":"publisher","first-page":"743","DOI":"10.21037\/qims.2018.09.02","volume":"8","author":"Q Zhang","year":"2018","unstructured":"Zhang, Q., Rezaei, K.A., Saraf, S.S., Chu, Z., Wang, F., Wang, R.K.: Ultra-wide optical coherence tomography angiography in diabetic retinopathy. Quant. Imaging Med. Surg. 8(8), 743 (2018)","journal-title":"Quant. Imaging Med. Surg."},{"key":"15_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1007\/978-3-030-32239-7_6","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Fu","year":"2019","unstructured":"Fu, H., et al.: Evaluation of retinal image quality assessment networks in different color-spaces. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 48\u201356. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_6"},{"issue":"1","key":"15_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-021-23458-5","volume":"12","author":"L Dai","year":"2021","unstructured":"Dai, L., et al.: A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat. Commun. 12(1), 1\u201311 (2021)","journal-title":"Nat. Commun."},{"issue":"6","key":"15_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2022.100512","volume":"3","author":"R Liu","year":"2022","unstructured":"Liu, R., et al.: DeepDRiD: diabetic retinopathy-grading and image quality estimation challenge. Patterns 3(6), 100512 (2022)","journal-title":"Patterns"},{"key":"15_CR6","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.971943","volume":"10","author":"B Sheng","year":"2022","unstructured":"Sheng, B., et al.: An overview of artificial intelligence in diabetic retinopathy and other ocular diseases. Front. Public Health 10, 971943 (2022)","journal-title":"Front. Public Health"},{"issue":"6","key":"15_CR7","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1007\/s10278-018-0084-9","volume":"31","author":"SK Saha","year":"2018","unstructured":"Saha, S.K., Fernando, B., Cuadros, J., Xiao, D., Kanagasingam, Y.: Automated quality assessment of colour fundus images for diabetic retinopathy screening in telemedicine. J. Digit. Imaging 31(6), 869\u2013878 (2018). https:\/\/doi.org\/10.1007\/s10278-018-0084-9","journal-title":"J. Digit. Imaging"},{"issue":"6","key":"15_CR8","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1016\/j.media.2006.09.006","volume":"10","author":"M Niemeijer","year":"2006","unstructured":"Niemeijer, M., Abramoff, M.D., van Ginneken, B.: Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening. Med. Image Anal. 10(6), 888\u2013898 (2006)","journal-title":"Med. Image Anal."},{"issue":"5","key":"15_CR9","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299\u20131312 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"15_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/978-3-642-13278-0_39","volume-title":"Advances in Neural Networks - ISNN 2010","author":"R Alejo","year":"2010","unstructured":"Alejo, R., Sotoca, J.M., Valdovinos, R.M., Toribio, P.: Edited nearest neighbor rule for improving neural networks classifications. In: Zhang, L., Lu, B.-L., Kwok, J. (eds.) ISNN 2010. LNCS, vol. 6063, pp. 303\u2013310. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-13278-0_39"},{"key":"15_CR11","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.ins.2014.08.051","volume":"291","author":"JA S\u00e1ez","year":"2015","unstructured":"S\u00e1ez, J.A., Luengo, J., Stefanowski, J., Herrera, F.: SMOTE-IPF: addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Inf. Sci. 291, 184\u2013203 (2015)","journal-title":"Inf. Sci."},{"key":"15_CR12","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ins.2017.05.008","volume":"409","author":"W-C Lin","year":"2017","unstructured":"Lin, W.-C., Tsai, C.-F., Ya-Han, H., Jhang, J.-S.: Clustering-based undersampling in class-imbalanced data. Inf. Sci. 409, 17\u201326 (2017)","journal-title":"Inf. Sci."},{"key":"15_CR13","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ins.2018.10.029","volume":"477","author":"C-F Tsai","year":"2019","unstructured":"Tsai, C.-F., Lin, W.-C., Ya-Han, H., Yao, G.-T.: Under-sampling class imbalanced datasets by combining clustering analysis and instance selection. Inf. Sci. 477, 47\u201354 (2019)","journal-title":"Inf. Sci."},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Yao, H., Hu, X., Li, X.: Enhancing pseudo label quality for semi-supervised domain-generalized medical image segmentation. arXiv preprint arXiv:2201.08657 (2022)","DOI":"10.1609\/aaai.v36i3.20217"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Yu, F.L., Sun, J., Li, A., Cheng, J., Wan, C., Liu, J.: Image quality classification for DR screening using deep learning. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 664\u2013667. IEEE (2017)","DOI":"10.1109\/EMBC.2017.8036912"},{"key":"15_CR16","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":"15_CR17","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"15_CR18","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":"15_CR19","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: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"}],"container-title":["Lecture Notes in Computer Science","Mitosis Domain Generalization and Diabetic Retinopathy Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-33658-4_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T08:04:10Z","timestamp":1685347450000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-33658-4_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031336577","9783031336584"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-33658-4_15","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":"30 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DRAC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Challenge on Diabetic Retinopathy 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":"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":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"drac2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/drac22.grand-challenge.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}