{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T08:58:21Z","timestamp":1765961901445,"version":"3.44.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049773","type":"print"},{"value":"9783032049780","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"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\/978-3-032-04978-0_58","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T16:17:09Z","timestamp":1758212229000},"page":"607-616","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Semantic-Aware Chest X-ray Report Generation with\u00a0Domain-Specific Lexicon and\u00a0Diversity-Controlled Retrieval"],"prefix":"10.1007","author":[{"given":"Baochang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuting","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heribert","family":"Schunkert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nassir","family":"Navab","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"58_CR1","doi-asserted-by":"crossref","unstructured":"Beltagy, I., Lo, K., Cohan, A.: Scibert: a pretrained language model for scientific text. arXiv preprint arXiv:1903.10676 (2019)","DOI":"10.18653\/v1\/D19-1371"},{"key":"58_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Z., Shen, Y., Song, Y., Wan, X.: Cross-modal memory networks for radiology report generation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 5904\u20135914 (2021)","DOI":"10.18653\/v1\/2021.acl-long.459"},{"key":"58_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Z., Song, Y., Chang, T.H., Wan, X.: Generating radiology reports via memory-driven transformer. arXiv preprint arXiv:2010.16056 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.112"},{"key":"58_CR4","doi-asserted-by":"crossref","unstructured":"Dong, Y., Pan, Y., Zhang, J., Xu, W.: Learning to read chest x-ray images from 16000+ examples using cnn. In: 2017 IEEE\/ACM International Conference On Connected Health: Applications, Systems And Engineering Technologies (CHASE), pp. 51\u201357. IEEE (2017)","DOI":"10.1109\/CHASE.2017.59"},{"key":"58_CR5","unstructured":"Endo, M., Krishnan, R., Krishna, V., Ng, A.Y., Rajpurkar, P.: Retrieval-based chest x-ray report generation using a pre-trained contrastive language-image model. In: Machine Learning for Health, pp. 209\u2013219. PMLR (2021)"},{"key":"58_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"58_CR7","unstructured":"Jain, S., et\u00a0al.: Radgraph: extracting clinical entities and relations from radiology reports. arXiv preprint arXiv:2106.14463 (2021)"},{"key":"58_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. Scientific data 6(1), 317 (2019)","DOI":"10.1038\/s41597-019-0322-0"},{"key":"58_CR9","doi-asserted-by":"crossref","unstructured":"Kulesza, A., Taskar, B., et\u00a0al.: Determinantal point processes for machine learning. Found. Trends\u00ae Mach. Learn. 5(2\u20133), 123\u2013286 (2012)","DOI":"10.1561\/2200000044"},{"key":"58_CR10","doi-asserted-by":"crossref","unstructured":"Li, C.Y., Liang, X., Hu, Z., Xing, E.P.: Knowledge-driven encode, retrieve, paraphrase for medical image report generation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a033, pp. 6666\u20136673 (2019)","DOI":"10.1609\/aaai.v33i01.33016666"},{"key":"58_CR11","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":"58_CR12","doi-asserted-by":"crossref","unstructured":"Liu, K., et al.: Structural entities extraction and patient indications incorporation for chest x-ray report generation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 433\u2013443. Springer (2024)","DOI":"10.1007\/978-3-031-72384-1_41"},{"key":"58_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/978-3-030-87196-3_50","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"L Luo","year":"2021","unstructured":"Luo, L., Chen, H., Zhou, Y., Lin, H., Heng, P.-A.: OXnet: Deep Omni-Supervised Thoracic Disease Detection from Chest X-Rays. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 537\u2013548. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_50"},{"key":"58_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102633","volume":"144","author":"A Nicolson","year":"2023","unstructured":"Nicolson, A., Dowling, J., Koopman, B.: Improving chest x-ray report generation by leveraging warm starting. Artifi. Intell. Med. 144, 102633 (2023)","journal-title":"Artifi. Intell. Med."},{"key":"58_CR15","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-bert: sentence embeddings using siamese bert-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3982\u20133992 (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"58_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106646","volume":"155","author":"FJM Shamrat","year":"2023","unstructured":"Shamrat, F.J.M., Azam, S., Karim, A., Ahmed, K., Bui, F.M., De Boer, F.: High-precision multiclass classification of lung disease through customized mobilenetv2 from chest x-ray images. Comput. Biol. Med. 155, 106646 (2023)","journal-title":"Comput. Biol. Med."},{"key":"58_CR17","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/RBME.2020.2987975","volume":"14","author":"F Shi","year":"2020","unstructured":"Shi, F., et al.: Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for covid-19. IEEE Rev. Biomed. Eng. 14, 4\u201315 (2020)","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"58_CR18","doi-asserted-by":"crossref","unstructured":"Shin, H.C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J., Summers, R.M.: Learning to read chest x-rays: recurrent neural cascade model for automated image annotation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2497\u20132506 (2016)","DOI":"10.1109\/CVPR.2016.274"},{"key":"58_CR19","volume-title":"Automated radiology report generation: a review of recent advances","author":"P Sloan","year":"2024","unstructured":"Sloan, P., Clatworthy, P., Simpson, E., Mirmehdi, M.: Automated radiology report generation: a review of recent advances. IEEE Rev. Biomed., Eng (2024)"},{"issue":"1","key":"58_CR20","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/S1386-5056(97)00048-8","volume":"46","author":"HJ Tange","year":"1997","unstructured":"Tange, H.J., Hasman, A., de Vries Robb\u00e9, P.F., Schouten, H.C.: Medical narratives in electronic medical records. Int. J. Med. Inf. 46(1), 7\u201329 (1997)","journal-title":"Int. J. Med. Inf."},{"key":"58_CR21","doi-asserted-by":"crossref","unstructured":"Tanida, T., M\u00fcller, P., Kaissis, G., Rueckert, D.: Interactive and explainable region-guided radiology report generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7433\u20137442 (2023)","DOI":"10.1109\/CVPR52729.2023.00718"},{"key":"58_CR22","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Summers, R.M.: Tienet: text-image embedding network for common thorax disease classification and reporting in chest x-rays. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9049\u20139058 (2018)","DOI":"10.1109\/CVPR.2018.00943"},{"key":"58_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102798","volume":"86","author":"S Yang","year":"2023","unstructured":"Yang, S., Wu, X., Ge, S., Zheng, Z., Zhou, S.K., Xiao, L.: Radiology report generation with a learned knowledge base and multi-modal alignment. Med. Image Analysis 86, 102798 (2023)","journal-title":"Med. Image Analysis"},{"key":"58_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102510","volume":"80","author":"S Yang","year":"2022","unstructured":"Yang, S., Wu, X., Ge, S., Zhou, S.K., Xiao, L.: Knowledge matters: chest radiology report generation with general and specific knowledge. Med. Image Analysis 80, 102510 (2022)","journal-title":"Med. Image Analysis"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04978-0_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:04:54Z","timestamp":1758233094000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04978-0_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032049773","9783032049780"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04978-0_58","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,19]]},"assertion":[{"value":"19 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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"}}]}}