{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T01:53:14Z","timestamp":1774403594336,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030720865","type":"print"},{"value":"9783030720872","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\/978-3-030-72087-2_42","type":"book-chapter","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T04:09:34Z","timestamp":1616645374000},"page":"475-486","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multimodal Brain Tumor Classification"],"prefix":"10.1007","author":[{"given":"Marvin","family":"Lerousseau","sequence":"first","affiliation":[]},{"given":"Eric","family":"Deutsch","sequence":"additional","affiliation":[]},{"given":"Nikos","family":"Paragios","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,26]]},"reference":[{"key":"42_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1007\/978-3-030-11723-8_42","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"A Bagari","year":"2019","unstructured":"Bagari, A., Kumar, A., Kori, A., Khened, M., Krishnamurthi, G.: A combined radio-histological approach for classification of low grade gliomas. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 416\u2013427. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11723-8_42"},{"issue":"8","key":"42_CR2","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","volume":"25","author":"G Campanella","year":"2019","unstructured":"Campanella, G., et al.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25(8), 1301\u20131309 (2019)","journal-title":"Nat. Med."},{"issue":"10","key":"42_CR3","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","volume":"24","author":"N Coudray","year":"2018","unstructured":"Coudray, N., et al.: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10), 1559\u20131567 (2018)","journal-title":"Nat. Med."},{"issue":"1\u20132","key":"42_CR4","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/S0004-3702(96)00034-3","volume":"89","author":"TG Dietterich","year":"1997","unstructured":"Dietterich, T.G., Lathrop, R.H., Lozano-P\u00e9rez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1\u20132), 31\u201371 (1997)","journal-title":"Artif. Intell."},{"key":"42_CR5","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.patcog.2018.07.022","volume":"84","author":"B Gecer","year":"2018","unstructured":"Gecer, B., Aksoy, S., Mercan, E., Shapiro, L.G., Weaver, D.L., Elmore, J.G.: Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern Recogn. 84, 345\u2013356 (2018)","journal-title":"Pattern Recogn."},{"issue":"10","key":"42_CR6","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1109\/34.58871","volume":"12","author":"LK Hansen","year":"1990","unstructured":"Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993\u20131001 (1990)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"42_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"key":"42_CR8","doi-asserted-by":"crossref","unstructured":"Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H.: Patch-based convolutional neural network for whole slide tissue image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2424\u20132433 (2016)","DOI":"10.1109\/CVPR.2016.266"},{"key":"42_CR9","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":"42_CR10","unstructured":"Ilse, M., Tomczak, J.M., Welling, M.: Attention-based deep multiple instance learning. arXiv preprint arXiv:1802.04712 (2018)"},{"key":"42_CR11","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"4","key":"42_CR12","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1016\/j.mric.2016.06.006","volume":"24","author":"A Kotrotsou","year":"2016","unstructured":"Kotrotsou, A., Zinn, P.O., Colen, R.R.: Radiomics in brain tumors: an emerging technique for characterization of tumor environment. Magn. Reson. Imaging Clin. 24(4), 719\u2013729 (2016)","journal-title":"Magn. Reson. Imaging Clin."},{"issue":"12","key":"42_CR13","doi-asserted-by":"publisher","first-page":"i52","DOI":"10.1093\/bioinformatics\/btw252","volume":"32","author":"OZ Kraus","year":"2016","unstructured":"Kraus, O.Z., Ba, J.L., Frey, B.J.: Classifying and segmenting microscopy images with deep multiple instance learning. Bioinformatics 32(12), i52\u2013i59 (2016)","journal-title":"Bioinformatics"},{"key":"42_CR14","doi-asserted-by":"crossref","unstructured":"Kurc, T., et\u00a0al.: Segmentation and classification indigital pathology for glioma research: challenges and deep learningapproaches. Front. Neurosci. 14 (2020)","DOI":"10.3389\/fnins.2020.00027"},{"issue":"4","key":"42_CR15","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin, P., et al.: Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48(4), 441\u2013446 (2012)","journal-title":"Eur. J. Cancer"},{"key":"42_CR16","doi-asserted-by":"crossref","unstructured":"Lerousseau, M., et al.: Weakly supervised multiple instance learning histopathological tumor segmentation. arXiv preprint arXiv:2004.05024 (2020)","DOI":"10.1007\/978-3-030-59722-1_45"},{"issue":"6","key":"42_CR17","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1007\/s00401-016-1545-1","volume":"131","author":"DN Louis","year":"2016","unstructured":"Louis, D.N., et al.: The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 131(6), 803\u2013820 (2016)","journal-title":"Acta Neuropathol."},{"key":"42_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/978-3-030-11723-8_41","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"A Momeni","year":"2019","unstructured":"Momeni, A., Thibault, M., Gevaert, O.: Dropout-enabled ensemble learning for multi-scale biomedical data. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 407\u2013415. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11723-8_41"},{"issue":"5","key":"42_CR19","doi-asserted-by":"publisher","first-page":"2108","DOI":"10.1109\/JBHI.2018.2885134","volume":"23","author":"Q Qi","year":"2018","unstructured":"Qi, Q., et al.: Label-efficient breast cancer histopathological image classification. IEEE J. Biomed. Health Inform. 23(5), 2108\u20132116 (2018)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"42_CR20","unstructured":"Ramon, J., De Raedt, L.: Multi instance neural networks. In: Proceedings of the ICML-2000 Workshop on Attribute-value and Relational Learning, pp. 53\u201360 (2000)"},{"key":"42_CR21","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.cogsys.2018.12.007","volume":"54","author":"M Talo","year":"2019","unstructured":"Talo, M., Baloglu, U.B., Y\u0131ld\u0131r\u0131m, \u00d6., Acharya, U.R.: Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn. Syst. Res. 54, 176\u2013188 (2019)","journal-title":"Cogn. Syst. Res."},{"key":"42_CR22","unstructured":"Tan, M., Le, Q.V.: Efficientnet: rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946 (2019)"},{"issue":"1","key":"42_CR23","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3390\/cancers11010111","volume":"11","author":"GS Tandel","year":"2019","unstructured":"Tandel, G.S., et al.: A review on a deep learning perspective in brain cancer classification. Cancers 11(1), 111 (2019)","journal-title":"Cancers"},{"key":"42_CR24","unstructured":"Zhang, C., Platt, J.C., Viola, P.A.: Multiple instance boosting for object detection. In: Advances in Neural Information Processing Systems, pp. 1417\u20131424 (2006)"},{"issue":"2","key":"42_CR25","doi-asserted-by":"publisher","first-page":"208","DOI":"10.3174\/ajnr.A5391","volume":"39","author":"M Zhou","year":"2018","unstructured":"Zhou, M., et al.: Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. Am. J. Neuroradiol. 39(2), 208\u2013216 (2018)","journal-title":"Am. J. Neuroradiol."},{"issue":"1\u20132","key":"42_CR26","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/S0004-3702(02)00190-X","volume":"137","author":"ZH Zhou","year":"2002","unstructured":"Zhou, Z.H., Wu, J., Tang, W.: Ensembling neural networks: many could be better than all. Artif. Intell. 137(1\u20132), 239\u2013263 (2002)","journal-title":"Artif. Intell."}],"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\/978-3-030-72087-2_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T01:04:03Z","timestamp":1774400643000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-72087-2_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030720865","9783030720872"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72087-2_42","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":"26 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"}]}}