{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T01:59:48Z","timestamp":1774490388115,"version":"3.50.1"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030720834","type":"print"},{"value":"9783030720841","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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-72084-1_37","type":"book-chapter","created":{"date-parts":[[2021,3,26]],"date-time":"2021-03-26T07:03:03Z","timestamp":1616742183000},"page":"412-423","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Multi-decoder Networks with Multi-denoising Inputs for Tumor Segmentation"],"prefix":"10.1007","author":[{"given":"Minh H.","family":"Vu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tufve","family":"Nyholm","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tommy","family":"L\u00f6fstedt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,27]]},"reference":[{"key":"37_CR1","doi-asserted-by":"crossref","unstructured":"Ali, H.M.: A new method to remove salt pepper noise in magnetic resonance images. In: 2016 11th International Conference on Computer Engineering Systems (ICCES), pp. 155\u2013160 (2016)","DOI":"10.1109\/ICCES.2016.7821992"},{"key":"37_CR2","unstructured":"Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The cancer imaging archive (2017) (2017)"},{"key":"37_CR3","unstructured":"Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Archive 286 (2017)"},{"key":"37_CR4","doi-asserted-by":"publisher","first-page":"170117","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, 170117 (2017)","journal-title":"Sci. Data"},{"key":"37_CR5","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. arXiv preprint arXiv:1811.02629 (2018)"},{"key":"37_CR6","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1097\/PDM.0b013e31818f071b","volume":"18","author":"X Castells","year":"2009","unstructured":"Castells, X., et al.: Automated brain tumor biopsy prediction using single-labeling CDNA microarrays-based gene expression profiling. Diagn. Mol. Pathol. 18, 206\u2013218 (2009)","journal-title":"Diagn. Mol. Pathol."},{"key":"37_CR7","doi-asserted-by":"crossref","unstructured":"Hausdorff, F.: Erweiterung einer stetigen Abbildung, pp. 555\u2013568. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-76807-4_16","DOI":"10.1007\/978-3-540-76807-4_16"},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"37_CR9","unstructured":"Isensee, F., et al.: batchgenerators\u2013a python framework for data augmentation, January 2020"},{"key":"37_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-030-11726-9_21","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"F Isensee","year":"2019","unstructured":"Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No new-net. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 234\u2013244. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_21"},{"key":"37_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-3-030-46640-4_22","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"Z Jiang","year":"2020","unstructured":"Jiang, Z., Ding, C., Liu, M., Tao, D.: Two-stage cascaded U-Net: 1st place solution to BraTS challenge 2019 segmentation task. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 231\u2013241. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46640-4_22"},{"key":"37_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"37_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-3-030-46640-4_36","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"R McKinley","year":"2020","unstructured":"McKinley, R., Rebsamen, M., Meier, R., Wiest, R.: Triplanar ensemble of 3D-to-2D CNNs with label-uncertainty for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 379\u2013387. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46640-4_36"},{"issue":"10","key":"37_CR14","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE TRans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE TRans. Med. Imaging"},{"key":"37_CR15","doi-asserted-by":"crossref","unstructured":"Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization (2018). http:\/\/arxiv.org\/abs\/1810.11654","DOI":"10.1007\/978-3-030-11726-9_28"},{"key":"37_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 \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":"37_CR17","doi-asserted-by":"crossref","unstructured":"Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings, pp. 958\u2013963 (2003)","DOI":"10.1109\/ICDAR.2003.1227801"},{"key":"37_CR18","unstructured":"Thurnher, M.: The 2007 WHO classification of tumors of the central nervous system\u2013what has changed? Am. J. Neuroradiol. (2012)"},{"key":"37_CR19","doi-asserted-by":"crossref","unstructured":"Vu, M.H., Grimbergen, G., Nyholm, T., L\u00f6fstedt, T.: Evaluation of multi-slice inputs to convolutional neural networks for medical image segmentation. arXiv preprint arXiv:1912.09287 (2019)","DOI":"10.1002\/mp.14391"},{"key":"37_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-3-030-46640-4_17","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"MH Vu","year":"2020","unstructured":"Vu, M.H., Nyholm, T., L\u00f6fstedt, T.: TuNet: end-to-end hierarchical brain tumor segmentation using cascaded networks. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 174\u2013186. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46640-4_17"},{"key":"37_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1007\/978-3-030-46640-4_20","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"Y-X Zhao","year":"2020","unstructured":"Zhao, Y.-X., Zhang, Y.-M., Liu, C.-L.: Bag of tricks for 3D MRI brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 210\u2013220. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46640-4_20"}],"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\/978-3-030-72084-1_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T01:07:17Z","timestamp":1774487237000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-72084-1_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030720834","9783030720841"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72084-1_37","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":"27 March 2021","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2020","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"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}