{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:16:58Z","timestamp":1775693818728,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032163691","type":"print"},{"value":"9783032163707","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-16370-7_19","type":"book-chapter","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:32:05Z","timestamp":1775691125000},"page":"213-222","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Patch-Level Brain Tumor Sub-region Classification Using Foundation Models Under Long-Tailed Data Distributions"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8232-8920","authenticated-orcid":false,"given":"Luis Carlos Rivera","family":"Monroy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3706-285X","authenticated-orcid":false,"given":"Martin","family":"Mayr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6449-7309","authenticated-orcid":false,"given":"Leonid","family":"Mill","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6992-2690","authenticated-orcid":false,"given":"Harald","family":"K\u00f6stler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9550-5284","authenticated-orcid":false,"given":"Andreas","family":"Maier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Abdel-Nabi, H., et al.: A comprehensive review of the deep learning-based tumor analysis approaches in histopathological images: segmentation, classification and multi-learning tasks. Clust. Comput. 26(5), 3145\u20133185 (2023)","DOI":"10.1007\/s10586-022-03951-2"},{"key":"19_CR2","doi-asserted-by":"publisher","unstructured":"Aboian, M., et al.: Miccai 2025 lighthouse challenge: brain tumor segmentation cluster of challenges (brats) (2025). https:\/\/doi.org\/10.5281\/ZENODO.13981215, https:\/\/zenodo.org\/doi\/10.5281\/zenodo.13981215","DOI":"10.5281\/ZENODO.13981215"},{"key":"19_CR3","doi-asserted-by":"publisher","unstructured":"Bakas, S., et al.: Brats-path challenge: assessing heterogeneous histopathologic brain tumor sub-regions (2024). https:\/\/doi.org\/10.48550\/ARXIV.2405.10871, https:\/\/arxiv.org\/abs\/2405.10871","DOI":"10.48550\/ARXIV.2405.10871"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020)","DOI":"10.3390\/info11020125"},{"key":"19_CR5","doi-asserted-by":"publisher","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). https:\/\/doi.org\/10.1038\/s41591-019-0508-1, http:\/\/dx.doi.org\/10.1038\/s41591-019-0508-1","DOI":"10.1038\/s41591-019-0508-1"},{"key":"19_CR6","doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785\u2013794. ACM, August 2016.https:\/\/doi.org\/10.1145\/2939672.2939785, http:\/\/dx.doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"19_CR7","doi-asserted-by":"publisher","unstructured":"Karargyris, A., et al.: Federated benchmarking of medical artificial intelligence with medperf. Nature Machine Intelligence 5(7), 799\u2013810 (2023). https:\/\/doi.org\/10.1038\/s42256-023-00652-2, http:\/\/dx.doi.org\/10.1038\/s42256-023-00652-2","DOI":"10.1038\/s42256-023-00652-2"},{"key":"19_CR8","unstructured":"Karasikov, M., et al.: Training state-of-the-art pathology foundation models with orders of magnitude less data. arXiv preprint arXiv:2504.05186 (2025), https:\/\/arxiv.org\/abs\/2504.05186"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Louis, D.N., et al.: The 2021 who classification of tumors of the central nervous system: a summary. Neuro Oncol. 23(8), 1231\u20131251 (2021)","DOI":"10.1093\/neuonc\/noab106"},{"key":"19_CR10","doi-asserted-by":"publisher","unstructured":"McGenity, C., et al.: Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. npj Digit. Med. 7(1) (2024). https:\/\/doi.org\/10.1038\/s41746-024-01106-8, http:\/\/dx.doi.org\/10.1038\/s41746-024-01106-8","DOI":"10.1038\/s41746-024-01106-8"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616(7956), 259\u2013265 (2023)","DOI":"10.1038\/s41586-023-05881-4"},{"key":"19_CR12","doi-asserted-by":"publisher","unstructured":"Morales, S., Engan, K., Naranjo, V.: Artificial intelligence in computational pathology \u2013 challenges and future directions. Digit. Sig. Process. 119, 103196 (2021). https:\/\/doi.org\/10.1016\/j.dsp.2021.103196, http:\/\/dx.doi.org\/10.1016\/j.dsp.2021.103196","DOI":"10.1016\/j.dsp.2021.103196"},{"key":"19_CR13","doi-asserted-by":"publisher","unstructured":"Network, T.C.G.A.R.: Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. New England J. Med. 372(26), 2481\u20132498 (2015). https:\/\/doi.org\/10.1056\/nejmoa1402121, http:\/\/dx.doi.org\/10.1056\/NEJMoa1402121","DOI":"10.1056\/nejmoa1402121"},{"key":"19_CR14","doi-asserted-by":"publisher","unstructured":"Price, M., et al.: Cbtrus statistical report: primary brain and other central nervous system tumors diagnosed in the united states in 2017\u20132021. Neuro-Oncol. 26(Supplement_6), vi1\u2013vi85 (2024). https:\/\/doi.org\/10.1093\/neuonc\/noae145, http:\/\/dx.doi.org\/10.1093\/neuonc\/noae145","DOI":"10.1093\/neuonc\/noae145"},{"key":"19_CR15","doi-asserted-by":"publisher","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1) (2019). https:\/\/doi.org\/10.1186\/s40537-019-0197-0, http:\/\/dx.doi.org\/10.1186\/s40537-019-0197-0","DOI":"10.1186\/s40537-019-0197-0"},{"key":"19_CR16","doi-asserted-by":"publisher","unstructured":"Supriyadi, M.R., et al.: A systematic literature review: exploring the challenges of ensemble model for medical imaging. BMC Med. Imaging 25(1) (2025). https:\/\/doi.org\/10.1186\/s12880-025-01667-4, http:\/\/dx.doi.org\/10.1186\/s12880-025-01667-4","DOI":"10.1186\/s12880-025-01667-4"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Vorontsov, E., et al.: A foundation model for clinical-grade computational pathology and rare cancers detection. Nat. Med. 30(10), 2924\u20132935 (2024)","DOI":"10.1038\/s41591-024-03141-0"},{"key":"19_CR18","doi-asserted-by":"publisher","unstructured":"Xiong, C., Chen, H., Sung, J.J.Y.: A survey of pathology foundation model: progress and future directions (2025). https:\/\/doi.org\/10.48550\/ARXIV.2504.04045, https:\/\/arxiv.org\/abs\/2504.04045","DOI":"10.48550\/ARXIV.2504.04045"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Xu, H., et al.: A whole-slide foundation model for digital pathology from real-world data. Nature 630(8015), 181\u2013188 (2024)","DOI":"10.1038\/s41586-024-07441-w"}],"container-title":["Lecture Notes in Computer Science","Segmentation, Classification, and Synthesis for Brain Tumors and Traumatic Brain Injuries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-16370-7_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:32:07Z","timestamp":1775691127000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-16370-7_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032163691","9783032163707"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-16370-7_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}