{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:04:33Z","timestamp":1767650673001,"version":"3.40.5"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030466428"},{"type":"electronic","value":"9783030466435"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-46643-5_26","type":"book-chapter","created":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T23:24:36Z","timestamp":1589844276000},"page":"266-273","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The Tumor Mix-Up in 3D Unet for Glioma Segmentation"],"prefix":"10.1007","author":[{"given":"Pengyu","family":"Yin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingdong","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaming","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,5,19]]},"reference":[{"issue":"July","key":"26_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4(July), 1\u201313 (2017). https:\/\/doi.org\/10.1038\/sdata.2017.117","journal-title":"Sci. Data"},{"key":"26_CR2","unstructured":"Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. CoRR abs\/1811.02629 (2018). http:\/\/arxiv.org\/abs\/1811.02629"},{"key":"26_CR3","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"},{"issue":"21","key":"26_CR4","doi-asserted-by":"publisher","first-page":"2683","DOI":"10.1101\/gad.1596707","volume":"21","author":"FB Furnari","year":"2007","unstructured":"Furnari, F.B., et al.: Malignant astrocytic glioma: genetics, biology, and paths to treatment. Genes Dev. 21(21), 2683\u20132710 (2007). https:\/\/doi.org\/10.1101\/gad.1596707","journal-title":"Genes Dev."},{"issue":"3","key":"26_CR5","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1109\/TMI.2011.2181857","volume":"31","author":"A Hamamci","year":"2011","unstructured":"Hamamci, A., Kucuk, N., Karaman, K., Engin, K., Unal, G.: Tumor-Cut: segmentation of brain tumors on contrast enhanced mr images for radiosurgery applications. IEEE Trans. Med. Imaging 31(3), 790\u2013804 (2011)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbel\u00e1ez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 447\u2013456 (2015)","DOI":"10.1109\/CVPR.2015.7298642"},{"issue":"5","key":"26_CR7","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1007\/s11548-015-1311-1","volume":"11","author":"M Havaei","year":"2015","unstructured":"Havaei, M., Larochelle, H., Poulin, P., Jodoin, P.-M.: Within-brain classification for brain tumor segmentation. Int. J. Comput. Assist. Radiol. Surg. 11(5), 777\u2013788 (2015). https:\/\/doi.org\/10.1007\/s11548-015-1311-1","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Isensee, F., Maier-Hein, K.H.: An attempt at beating the 3D U-Net. arXiv preprint arXiv:1908.02182 (2019)","DOI":"10.24926\/548719.001"},{"key":"26_CR9","unstructured":"Isensee, F., Petersen, J., Kohl, S.A., J\u00e4ger, P.F., Maier-Hein, K.H.: nnU-Net: breaking the spell on successful medical image segmentation. arXiv preprint arXiv:1904.08128 (2019)"},{"issue":"10","key":"26_CR10","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1016\/j.fss.2008.11.016","volume":"160","author":"H Khotanlou","year":"2009","unstructured":"Khotanlou, H., Colliot, O., Atif, J., Bloch, I.: 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets Syst. 160(10), 1457\u20131473 (2009)","journal-title":"Fuzzy Sets Syst."},{"key":"26_CR11","volume-title":"Principles of Magnetic Resonance Imaging: A Signal Processing Perspective","author":"ZP Liang","year":"2000","unstructured":"Liang, Z.P., Lauterbur, P.C.: Principles of Magnetic Resonance Imaging: A Signal Processing Perspective. SPIE Optical Engineering Press, Bellingham (2000)"},{"key":"26_CR12","unstructured":"Mazumdar, I.: Automated brain tumour segmentation using deep fully convolutional residual networks. arXiv preprint arXiv:1908.04250 (2019)"},{"issue":"10","key":"26_CR13","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2015). https:\/\/doi.org\/10.1109\/TMI.2014.2377694","journal-title":"IEEE Trans. Med. Imaging"},{"key":"26_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/978-3-030-11726-9_28","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"A Myronenko","year":"2019","unstructured":"Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311\u2013320. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_28"},{"key":"26_CR15","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 \u2013 MICCAI 2015","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"},{"key":"26_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-319-67558-9_28","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"CH Sudre","year":"2017","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 240\u2013248. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_28"},{"issue":"2","key":"26_CR17","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/s12021-014-9245-2","volume":"13","author":"NJ Tustison","year":"2015","unstructured":"Tustison, N.J., et al.: Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13(2), 209\u2013225 (2015). https:\/\/doi.org\/10.1007\/s12021-014-9245-2","journal-title":"Neuroinformatics"},{"key":"26_CR18","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"}],"container-title":["Lecture Notes in Computer Science","Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-46643-5_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,18]],"date-time":"2025-05-18T22:03:28Z","timestamp":1747605808000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-46643-5_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030466428","9783030466435"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-46643-5_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 May 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BrainLes","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International MICCAI Brainlesion Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.brainlesion-workshop.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}