{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T18:37:00Z","timestamp":1779907020042,"version":"3.53.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031789540","type":"print"},{"value":"9783031789557","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-78955-7_12","type":"book-chapter","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T14:19:14Z","timestamp":1737987554000},"page":"115-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Knowledge Graph-Enhanced Vision-to-Language Multimodal Models for\u00a0Radiology Report Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2064-0107","authenticated-orcid":false,"given":"Yongli","family":"Mou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,28]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Bodenreider, O.: The unified medical language system (umls): integrating biomedical terminology. Nucleic Acids Res. 32(suppl_1), D267\u2013D270 (2004)","DOI":"10.1093\/nar\/gkh061"},{"key":"12_CR2","doi-asserted-by":"publisher","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-Vaya, 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":"12_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102444","volume":"79","author":"X Chen","year":"2022","unstructured":"Chen, X., et al.: Recent advances and clinical applications of deep learning in medical image analysis. Med. Image Anal. 79, 102444 (2022)","journal-title":"Med. Image Anal."},{"issue":"2","key":"12_CR4","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":"12_CR5","doi-asserted-by":"crossref","unstructured":"Girdhar, R., et al.: Imagebind: one embedding space to bind them all. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15180\u201315190 (2023)","DOI":"10.1109\/CVPR52729.2023.01457"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Irvin, J., et\u00a0al.: Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 590\u2013597 (2019)","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"12_CR7","unstructured":"Jain, S., et\u00a0al.: Radgraph: Extracting clinical entities and relations from radiology reports. arXiv preprint arXiv:2106.14463 (2021)"},{"key":"12_CR8","doi-asserted-by":"crossref","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), 317 (2019)","DOI":"10.1038\/s41597-019-0322-0"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., et al.: Mimic-cxr-jpg, a large publicly available database of labeled chest radiographs. arXiv preprint arXiv:1901.07042 (2019)","DOI":"10.1038\/s41597-019-0322-0"},{"key":"12_CR10","unstructured":"Li, J., Li, D., Savarese, S., Hoi, S.: Blip-2: bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597 (2023)"},{"key":"12_CR11","unstructured":"Li, J., Li, D., Xiong, C., Hoi, S.: Blip: bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International Conference on Machine Learning, pp. 12888\u201312900. PMLR (2022)"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Li, M., Lin, B., Chen, Z., Lin, H., Liang, X., Chang, X.: Dynamic graph enhanced contrastive learning for chest x-ray report generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3334\u20133343 (2023)","DOI":"10.1109\/CVPR52729.2023.00325"},{"key":"12_CR13","unstructured":"Li, Y., et al.: Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. arXiv preprint arXiv:2110.05208 (2021)"},{"key":"12_CR14","unstructured":"Li, Y., Liang, X., Hu, Z., Xing, E.P.: Hybrid retrieval-generation reinforced agent for medical image report generation. Adv. Neural Inform. Process. Syst. 31 (2018)"},{"key":"12_CR15","unstructured":"Liu, C., Tian, Y., Song, Y.: A systematic review of deep learning-based research on radiology report generation. arXiv preprint arXiv:2311.14199 (2023)"},{"key":"12_CR16","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13753\u201313762 (2021)","DOI":"10.1109\/CVPR46437.2021.01354"},{"key":"12_CR17","unstructured":"Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. arXiv preprint arXiv:2304.08485 (2023)"},{"key":"12_CR18","unstructured":"Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., Wu, X.: Unifying large language models and knowledge graphs: A roadmap. IEEE Trans. Knowl. Data Eng. (2024)","DOI":"10.1109\/TKDE.2024.3352100"},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Peng, C., Xia, F., Naseriparsa, M., Osborne, F.: Knowledge graphs: opportunities and challenges. Artifi. Intell. Rev. 1\u201332 (2023)","DOI":"10.1007\/s10462-023-10465-9"},{"key":"12_CR21","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"issue":"13","key":"12_CR22","doi-asserted-by":"publisher","first-page":"7441","DOI":"10.1007\/s00521-021-05943-6","volume":"33","author":"S Singh","year":"2021","unstructured":"Singh, S., Karimi, S., Ho-Shon, K., Hamey, L.: Show, tell and summarise: learning to generate and summarise radiology findings from medical images. Neural Comput. Appl. 33(13), 7441\u20137465 (2021). https:\/\/doi.org\/10.1007\/s00521-021-05943-6","journal-title":"Neural Comput. Appl."},{"issue":"1","key":"12_CR23","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1109\/TPAMI.2022.3148210","volume":"45","author":"M Stefanini","year":"2022","unstructured":"Stefanini, M., Cornia, M., Baraldi, L., Cascianelli, S., Fiameni, G., Cucchiara, R.: From show to tell: a survey on deep learning-based image captioning. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 539\u2013559 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"20","key":"12_CR24","first-page":"10","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al.: Graph attention networks. Stat 1050(20), 10\u201348550 (2017)","journal-title":"Stat"},{"key":"12_CR25","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097\u20132106 (2017)","DOI":"10.1109\/CVPR.2017.369"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Xu, Y., Namazifar, M., Hazarika, D., Padmakumar, A., Liu, Y., Hakkani-T\u00fcr, D.: Kilm: knowledge injection into encoder-decoder language models. arXiv preprint arXiv:2302.09170 (2023)","DOI":"10.18653\/v1\/2023.acl-long.275"},{"key":"12_CR27","doi-asserted-by":"crossref","unstructured":"Yasunaga, M., Ren, H., Bosselut, A., Liang, P., Leskovec, J.: Qa-gnn: reasoning with language models and knowledge graphs for question answering. arXiv preprint arXiv:2104.06378 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.45"},{"key":"12_CR28","doi-asserted-by":"publisher","unstructured":"You, K., et al.: Cxr-clip: toward large scale chest x-ray language-image pre-training. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 101\u2013111. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-43895-0_10","DOI":"10.1007\/978-3-031-43895-0_10"},{"key":"12_CR29","unstructured":"Zhang, X., et al.: Greaselm: graph reasoning enhanced language models. In: International Conference on Learning Representations (2021)"},{"key":"12_CR30","unstructured":"Zhang, Y., Sui, E., Yeung-Levy, S.: Connect, collapse, corrupt: learning cross-modal tasks with uni-modal data. arXiv preprint arXiv:2401.08567 (2024)"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web: ESWC 2024 Satellite Events"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78955-7_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T14:19:27Z","timestamp":1737987567000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78955-7_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031789540","9783031789557"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78955-7_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"28 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ESWC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Semantic Web Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hersonissos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"esws2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.eswc-conferences.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}