{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:49:37Z","timestamp":1743144577315,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031581700"},{"type":"electronic","value":"9783031581717"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-58171-7_9","type":"book-chapter","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T12:02:19Z","timestamp":1714132939000},"page":"84-94","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Knowledge Graph Embeddings for\u00a0Multi-lingual Structured Representations of\u00a0Radiology Reports"],"prefix":"10.1007","author":[{"given":"Tom","family":"van Sonsbeek","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiantong","family":"Zhen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcel","family":"Worring","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,27]]},"reference":[{"key":"9_CR1","first-page":"72","volume":"2019","author":"E Alsentzer","year":"2019","unstructured":"Alsentzer, E., et al.: Publicly available clinical BERT embeddings. NAACL HLT 2019, 72 (2019)","journal-title":"NAACL HLT"},{"issue":"3","key":"9_CR2","first-page":"229","volume":"17","author":"AR Aronson","year":"2010","unstructured":"Aronson, A.R., Lang, F.M.: An overview of MetaMap: historical perspective and recent advances. JAMIA 17(3), 229\u2013236 (2010)","journal-title":"JAMIA"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Beam, A.L., et al.: Clinical concept embeddings learned from massive sources of multimodal medical data. In: Pacific Symposium on Biocomputing 2020, pp. 295\u2013306. World Scientific (2019)","DOI":"10.1142\/9789811215636_0027"},{"key":"9_CR4","doi-asserted-by":"publisher","first-page":"D267","DOI":"10.1093\/nar\/gkh061","volume":"32","author":"O Bodenreider","year":"2004","unstructured":"Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32, D267\u2013D270 (2004)","journal-title":"Nucleic Acids Res."},{"key":"9_CR5","doi-asserted-by":"publisher","first-page":"101797","DOI":"10.1016\/j.media.2020.101797","volume":"66","author":"A Bustos","year":"2020","unstructured":"Bustos, A., Pertusa, A., Salinas, J.M., de la Iglesia-Vay\u00e1, M.: PadChest: a large chest x-ray image dataset with multi-label annotated reports. Med. Image Anal. 66, 101797 (2020)","journal-title":"Med. Image Anal."},{"key":"9_CR6","unstructured":"Carrino, C.P., et al.: Biomedical and clinical language models for Spanish: on the benefits of domain-specific pretraining in a mid-resource scenario (2021)"},{"issue":"1","key":"9_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-021-01533-7","volume":"21","author":"A Casey","year":"2021","unstructured":"Casey, A., et al.: A systematic review of natural language processing applied to radiology reports. BMC Med. Inform. Decis. Mak. 21(1), 1\u201318 (2021)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"9_CR8","unstructured":"Ca\u00f1ete, J., Chaperon, G., Fuentes, R., Ho, J.H., Kang, H., P\u00e9rez, J.: Spanish pre-trained BERT model and evaluation data. In: PML4DC at ICLR 2020 (2020)"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Chang, D., Bala\u017eevi\u0107, I., Allen, C., Chawla, D., Brandt, C., Taylor, R.A.: Benchmark and best practices for biomedical knowledge graph embeddings. In: Proceedings of the Conference Association for Computational Linguistics Meeting, vol.\u00a02020, p. 167. NIH Public Access (2020)","DOI":"10.18653\/v1\/2020.bionlp-1.18"},{"issue":"2","key":"9_CR10","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1093\/jamia\/ocv080","volume":"23","author":"D Demner-Fushman","year":"2016","unstructured":"Demner-Fushman, D., et al.: Preparing a collection of radiology examinations for distribution and retrieval. J. Am. Med. Inform. Assoc. 23(2), 304\u2013310 (2016)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"9_CR11","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding, pp. 4171\u20134186 (2019)"},{"key":"9_CR12","unstructured":"Gu, Y., et al.: Domain-specific language model pretraining for biomedical natural language processing (2020)"},{"issue":"1","key":"9_CR13","first-page":"1","volume":"3","author":"Y Gu","year":"2021","unstructured":"Gu, Y., et al.: Domain-specific language model pretraining for biomedical natural language processing. Trans. Comput. Healthcare 3(1), 1\u201323 (2021)","journal-title":"Trans. Comput. Healthcare"},{"key":"9_CR14","unstructured":"Heilig, N., Kirchhoff, J., Stumpe, F., Plepi, J., Flek, L., Paulheim, H.: Refining diagnosis paths for medical diagnosis based on an augmented knowledge graph. arXiv:2204.13329 (2022)"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Hu, J., et al.: Word graph guided summarization for radiology findings. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 4980\u20134990 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.441"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Hu, J., Li, Z., Chen, Z., Li, Z., Wan, X., Chang, T.H.: Graph enhanced contrastive learning for radiology findings summarization. arXiv:2204.00203 (2022)","DOI":"10.18653\/v1\/2022.acl-long.320"},{"key":"9_CR17","unstructured":"Jain, S., et al.: RadGraph: extracting clinical entities and relations from radiology reports. In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1) (2021)"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Ji, S., Pan, S., Cambria, E., Marttinen, P., Philip, S.Y.: A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Netw. Learn. Syst. (2021)","DOI":"10.1109\/TNNLS.2021.3070843"},{"issue":"1","key":"9_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-019-0322-0","volume":"6","author":"AE Johnson","year":"2019","unstructured":"Johnson, A.E., et al.: MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci. Data 6(1), 1\u20138 (2019)","journal-title":"Sci. Data"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Kale, K., et al.: Knowledge graph construction and its application in automatic radiology report generation from radiologist\u2019s dictation. arXiv preprint:2206.06308 (2022)","DOI":"10.2139\/ssrn.4138310"},{"issue":"4","key":"9_CR21","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2020)","journal-title":"Bioinformatics"},{"key":"9_CR22","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Liu, F., Wu, X., Ge, S., Fan, W., Zou, Y.: Exploring and distilling posterior and prior knowledge for radiology report generation. In: CVPR, pp. 13753\u201313762 (2021)","DOI":"10.1109\/CVPR46437.2021.01354"},{"key":"9_CR24","first-page":"16266","volume":"34","author":"F Liu","year":"2021","unstructured":"Liu, F., et al.: Auto-encoding knowledge graph for unsupervised medical report generation. NeurIPS 34, 16266\u201316279 (2021)","journal-title":"NeurIPS"},{"key":"9_CR25","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"issue":"6","key":"9_CR26","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1093\/bioinformatics\/btz853","volume":"36","author":"N Perez","year":"2019","unstructured":"Perez, N., et al.: Cross-lingual semantic annotation of biomedical literature: experiments in Spanish and English. Bioinformatics 36(6), 1872\u20131880 (2019)","journal-title":"Bioinformatics"},{"key":"9_CR27","unstructured":"Prabhakar, C., et al.: Structured knowledge graphs for classifying unseen patterns in radiographs. In: GeoMeDIA (2022)"},{"key":"9_CR28","unstructured":"Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. In: NeurIPS, pp. 3483\u20133491 (2015)"},{"key":"9_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1007\/978-3-030-78191-0_26","volume-title":"Information Processing in Medical Imaging","author":"T van Sonsbeek","year":"2021","unstructured":"van Sonsbeek, T., Zhen, X., Worring, M., Shao, L.: Variational knowledge distillation for disease classification in chest X-rays. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds.) IPMI 2021. LNCS, vol. 12729, pp. 334\u2013345. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78191-0_26"},{"key":"9_CR30","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)"},{"key":"9_CR31","doi-asserted-by":"crossref","unstructured":"Yan, S.: Memory-aligned knowledge graph for clinically accurate radiology image report generation. In: BioNLP, pp. 116\u2013122 (2022)","DOI":"10.18653\/v1\/2022.bionlp-1.11"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Yang, S., Wu, X., Ge, S., Zhou, S.K., Xiao, L.: Knowledge matters: radiology report generation with general and specific knowledge. arXiv:2112.15009 (2021)","DOI":"10.1016\/j.media.2022.102510"},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, D., Ren, A., Liang, J., Liu, Q., Wang, H., Ma, Y.: Improving medical x-ray report generation by using knowledge graph. Appl. Sci. 12(21) (2022)","DOI":"10.3390\/app122111111"},{"issue":"1","key":"9_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-019-0055-0","volume":"6","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Chen, Q., Yang, Z., Lin, H., Lu, Z.: BioWordVec, improving biomedical word embeddings with subword information and MeSH. Sci. data 6(1), 1\u20139 (2019)","journal-title":"Sci. data"},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, X., Xu, Z., Yu, Q., Yuille, A., Xu, D.: When radiology report generation meets knowledge graph. In: AAAI, vol. 34, pp. 12910\u201312917 (2020)","DOI":"10.1609\/aaai.v34i07.6989"},{"key":"9_CR36","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57\u201381 (2020)","journal-title":"AI Open"}],"container-title":["Lecture Notes in Computer Science","Data Augmentation, Labelling, and Imperfections"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-58171-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T12:12:35Z","timestamp":1714133555000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-58171-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031581700","9783031581717"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-58171-7_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"27 April 2024","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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","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":"2250","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":"730","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":"32% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}