{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T11:27:10Z","timestamp":1763810830983,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031090011"},{"type":"electronic","value":"9783031090028"}],"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\/978-3-031-09002-8_11","type":"book-chapter","created":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T12:05:34Z","timestamp":1657800334000},"page":"116-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Disparity Autoencoders for Multi-class Brain Tumor Segmentation"],"prefix":"10.1007","author":[{"given":"Chandan Ganesh","family":"Bangalore Yogananda","sequence":"first","affiliation":[]},{"given":"Yudhajit","family":"Das","sequence":"additional","affiliation":[]},{"given":"Benjamin C.","family":"Wagner","sequence":"additional","affiliation":[]},{"given":"Sahil S.","family":"Nalawade","sequence":"additional","affiliation":[]},{"given":"Divya","family":"Reddy","sequence":"additional","affiliation":[]},{"given":"James","family":"Holcomb","sequence":"additional","affiliation":[]},{"given":"Marco C.","family":"Pinho","sequence":"additional","affiliation":[]},{"given":"Baowei","family":"Fei","sequence":"additional","affiliation":[]},{"given":"Ananth J.","family":"Madhuranthakam","sequence":"additional","affiliation":[]},{"given":"Joseph A.","family":"Maldjian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,15]]},"reference":[{"issue":"3","key":"11_CR1","doi-asserted-by":"publisher","first-page":"166","DOI":"10.3322\/caac.20069","volume":"60","author":"EG Van Meir","year":"2010","unstructured":"Van Meir, E.G., Hadjipanayis, C.G., Norden, A.D., Shu, H.K., Wen, P.Y., Olson, J.J.: Exciting new advances in neuro-oncology: the avenue to a cure for malignant glioma. CA Cancer J. Clin. 60(3), 166\u2013193 (2010). https:\/\/doi.org\/10.3322\/caac.20069","journal-title":"CA Cancer J. Clin."},{"key":"11_CR2","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, 1\u201313 (2017)","journal-title":"Sci. Data"},{"key":"11_CR3","doi-asserted-by":"publisher","first-page":"105809","DOI":"10.1016\/j.cmpb.2020.105809","volume":"198","author":"A Khosravanian","year":"2021","unstructured":"Khosravanian, A., et al.: Fast level set method for glioma brain tumor segmentation based on superpixel fuzzy clustering and lattice boltzmann method. Comput. Methods Programs Biomed. 198, 105809 (2021)","journal-title":"Comput. Methods Programs Biomed."},{"key":"11_CR4","doi-asserted-by":"publisher","first-page":"2224","DOI":"10.1109\/TMI.2018.2824243","volume":"37","author":"Z Tang","year":"2018","unstructured":"Tang, Z., et al.: Multi-atlas segmentation of MR tumor brain images using low-rank based image recovery. IEEE Trans. Med. Imaging 37, 2224\u20132235 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Pei, L., et al.: Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI. Proc. SPIE Int. Soc. Opt. Eng. 10134 (2017)","DOI":"10.1117\/12.2254034"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. BrainLes 2017, LNCS 10670, pp 450\u2013462. https:\/\/doi.org\/10.1007\/978-3-319-75238-9_38 (2018)","DOI":"10.1007\/978-3-319-75238-9_38"},{"key":"11_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1007\/978-3-319-75238-9_38","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"K Kamnitsas","year":"2018","unstructured":"Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 450\u2013462. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75238-9_38"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV). pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"11_CR9","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","volume":"35","author":"S Pereira","year":"2016","unstructured":"Pereira, S., et al.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35, 1240\u20131251 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"11_CR10","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18\u201331 (2017)","journal-title":"Med. Image Anal."},{"key":"11_CR11","doi-asserted-by":"publisher","first-page":"5234","DOI":"10.1002\/mp.12481","volume":"44","author":"Y Zhuge","year":"2017","unstructured":"Zhuge, Y., et al.: Brain tumor segmentation using holistically nested neural networks in MRI images. Med. Phys. 44, 5234\u20135243 (2017)","journal-title":"Med. Phys."},{"key":"11_CR12","first-page":"36","volume":"36","author":"D Zikic","year":"2014","unstructured":"Zikic, D., et al.: Segmentation of brain tumor tissues with convolutional neural networks. Proc. MICCAI-BRATS 36, 36\u201339 (2014)","journal-title":"Proc. MICCAI-BRATS"},{"key":"11_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/978-3-319-42016-5_6","volume-title":"Medical Computer Vision: Algorithms for Big Data","author":"P Dvo\u0159\u00e1k","year":"2016","unstructured":"Dvo\u0159\u00e1k, P., Menze, B.: Local structure prediction with convolutional neural networks for multimodal brain tumor segmentation. In: Menze, B., Langs, G., Montillo, A., Kelm, M., M\u00fcller, H., Zhang, S., Cai, W., Metaxas, D. (eds.) MCV 2015. LNCS, vol. 9601, pp. 59\u201371. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-42016-5_6"},{"key":"11_CR14","unstructured":"Baid, U., et al.: The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:210702314 (2021)"},{"key":"11_CR15","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, 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"11_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.1","volume":"4","author":"CT Lloyd","year":"2017","unstructured":"Lloyd, C.T., Sorichetta, A., Tatem, A.J.: High resolution global gridded data for use in population studies. Sci. data 4, 1\u201317 (2017)","journal-title":"Sci. data"},{"key":"11_CR17","unstructured":"Bakas, S., Akbari, H., Sotiras, A.: Segmentation labels for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive (2017)"},{"key":"11_CR18","unstructured":"Bakas, S., et al.: Segmentation labels for the pre-operative scans of the TCGA-GBM collection (2017)"},{"key":"11_CR19","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.neuroimage.2014.05.044","volume":"99","author":"NJ Tustison","year":"2014","unstructured":"Tustison, N.J., et al.: Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. Neuroimage 99, 166\u2013179 (2014)","journal-title":"Neuroimage"},{"key":"11_CR20","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":"11_CR21","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":"11_CR22","doi-asserted-by":"crossref","unstructured":"Wegmayr, V.A.S., Buhmann, J., Nicholas, P.: Classification of brain MRI with big data and deep 3D convolutional neural networks. In: Mori, K. (Ed) Published in SPIE Proceedings, Medical Imaging 2018: Computer-Aided Diagnosis, pp. 1057501 (2018)","DOI":"10.1117\/12.2293719"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Feng, X., Yang, J., Lipton, Z.C., Small, S.A., Provenzano, F.A.: Deep learning on MRI affirms the prominence of the hippocampal formation in Alzheimer\u2019s disease classification. bioRxiv 2018, 456277 (2018)","DOI":"10.1101\/456277"},{"key":"11_CR24","unstructured":"ea Chollet, F., Keras, C.P.: GitHub repository (2015)"},{"key":"11_CR25","unstructured":"Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16), pp. 265\u2013283 (2016)"},{"key":"11_CR26","unstructured":"Kingma, D.P., Adam, B.J.: A method for stochastic optimization. arXiv preprint arXiv:14126980 (2014)"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"J\u00e9gou, S., et al.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition workshops, pp. 11\u201319 (2017)","DOI":"10.1109\/CVPRW.2017.156"},{"key":"11_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1007\/978-3-030-11726-9_40","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"R McKinley","year":"2019","unstructured":"McKinley, R., Meier, R., Wiest, R.: Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 456\u2013465. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_40"},{"key":"11_CR29","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1093\/neuonc\/noz199","volume":"22","author":"CG Bangalore Yogananda","year":"2020","unstructured":"Bangalore Yogananda, C.G., et al.: A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas. Neuro Oncol 22, 402\u2013411 (2020)","journal-title":"Neuro Oncol"},{"key":"11_CR30","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, 1993\u20132024 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"11_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1007\/978-3-319-75238-9_16","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"G Wang","year":"2018","unstructured":"Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 178\u2013190. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75238-9_16"}],"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\/978-3-031-09002-8_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T15:42:41Z","timestamp":1676130161000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-09002-8_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031090011","9783031090028"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-09002-8_11","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 July 2022","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.brainlesion-workshop.org\/?msclkid=7759e32ed14111ecba82c5ba435279db","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","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"151","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":"91","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":"60% - 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":"1.5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}