{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:19:46Z","timestamp":1758586786375,"version":"3.44.0"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032059963","type":"print"},{"value":"9783032059970","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"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\/978-3-032-05997-0_11","type":"book-chapter","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T07:23:39Z","timestamp":1758525819000},"page":"119-129","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FetalExtract-LLM: Structured Information Extraction from\u00a0Free-Text Fetal MRI Reports Based on\u00a0Privacy-Ensuring Open-Weights Large Language Models"],"prefix":"10.1007","author":[{"given":"Mingxuan","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yijin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Juncheng","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Hongjia","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yiming","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Haoxiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yifei","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xuguang","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Haibo","family":"Qu","sequence":"additional","affiliation":[]},{"given":"Qiyuan","family":"Tian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"11_CR1","unstructured":"Anthropic: Introducing claude 4. https:\/\/www.anthropic.com\/news\/introducing-claude-4 (2025), accessed: 2025-06-17"},{"key":"11_CR2","unstructured":"Bai, X., Liu, M., Chen, Y., Yang, H., Tian, Q.: Chest-OMDL: Organ-specific multidisease detection and localization in chest computed tomography using weakly supervised deep learning from free-text radiology report. In: Medical Imaging with Deep Learning (2025), https:\/\/openreview.net\/forum?id=ns6nq592HX"},{"key":"11_CR3","unstructured":"Balasubramanian, J.B., Adams, D., Roxanis, I., de\u00a0Gonzalez, A.B., Coulson, P., Almeida, J.S., Garc\u00eda-Closas, M.: Leveraging large language models for structured information extraction from pathology reports (2025), https:\/\/arxiv.org\/abs\/2502.12183"},{"key":"11_CR4","doi-asserted-by":"publisher","unstructured":"Chen, Y., Yang, H., Pan, H., Siddiqui, F., Verdone, A., Zhang, Q., Chopra, S., Zhao, C., Shen, Y.: Burextract-llama: An llm for clinical concept extraction in breast ultrasound reports. In: Proceedings of the 1st International Workshop on Multimedia Computing for Health and Medicine. p. 53\u201358. MCHM\u201924, Association for Computing Machinery, New York, NY, USA (2024). https:\/\/doi.org\/10.1145\/3688868.3689200","DOI":"10.1145\/3688868.3689200"},{"key":"11_CR5","unstructured":"DeepSeek-AI: Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning (2025), https:\/\/arxiv.org\/abs\/2501.12948"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Chang, B., et\u00a0al.: A survey on in-context learning. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. pp. 1107\u20131128 (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.64"},{"key":"11_CR7","doi-asserted-by":"publisher","unstructured":"Flanders, A.E., Wang, X., Wu, C.C., Kitamura, F.C., Shih, G., Mongan, J., Peng, Y.: The evolution of radiology image annotation in the era of large language models. Radiology. Artificial Intelligence 7(4), e240631 (2025). https:\/\/doi.org\/10.1148\/ryai.240631, https:\/\/doi.org\/10.1148\/ryai.240631","DOI":"10.1148\/ryai.240631"},{"key":"11_CR8","unstructured":"Google: Gemini 2.5 pro: Access google\u2019s latest preview ai model. https:\/\/blog.google\/products\/gemini\/gemini-2-5-pro-updates\/ (2025), accessed: 2025-06-17"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Grothey, B., Odenkirchen, J., Brkic, A., Sch\u00f6mig-Markiefka, B., Quaas, A., Buettner, R., Tolkach, Y.: Comprehensive testing of large language models for extraction of structured data in pathology. Communications Medicine 5 (2025), https:\/\/api.semanticscholar.org\/CorpusID:277464469","DOI":"10.1038\/s43856-025-00808-8"},{"key":"11_CR10","unstructured":"Hayou, S., Ghosh, N., Yu, B.: LoRA+: Efficient low rank adaptation of large models. In: Salakhutdinov, R., Kolter, Z., Heller, K., Weller, A., Oliver, N., Scarlett, J., Berkenkamp, F. (eds.) Proceedings of the 41st International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a0235, pp. 17783\u201317806. PMLR (21\u201327 Jul 2024), https:\/\/proceedings.mlr.press\/v235\/hayou24a.html"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Howell, L., Zarei, A., Wah, T.M., Chandler, J.H., Karthik, S., Court, Z., Ng, H., McLaughlan, J.R.: Radex: a rule-based clinical and radiology data extraction tool demonstrated on thyroid ultrasound reports. European Radiology pp. 1\u201312 (2025)","DOI":"10.1007\/s00330-025-11416-4"},{"key":"11_CR12","unstructured":"Li, Y., Liu, M., Yang, H., Li, H., Bai, X., Liao, Y., Qu, H., Tian, Q.: Anatomy-guided test-time adaptation for automated fetal brain MRI morphometry. In: Medical Imaging with Deep Learning - Short Papers (2025), https:\/\/openreview.net\/forum?id=iLBipDelQu"},{"issue":"3","key":"11_CR13","first-page":"265","volume":"88","author":"CE Lipscomb","year":"2000","unstructured":"Lipscomb, C.E.: Medical subject headings (mesh). Bull. Med. Libr. Assoc. 88(3), 265 (2000)","journal-title":"Bull. Med. Libr. Assoc."},{"key":"11_CR14","unstructured":"Liu, M., Li, H., Li, Z., Yang, H., Zheng, J., Qu, H., Tian, Q.: Unsupervised Fetal Brain MRI Quality Assessment based on Orientation Prediction Uncertainty. In: 2025 OHBM Annual Meeting. Brisbane, Australia (Jun 2025), https:\/\/hal.science\/hal-04974115"},{"key":"11_CR15","unstructured":"Liu, M., Li, H., Li, Z., Yang, H., Zheng, J., Zhang, X., Tian, Q.: Image Quality Assessment using an Orientation Recognition Network for Fetal MRI. In: 2024 ISMRM & ISMRT Annual Meeting & Exhibition. Singapore, Singapore (May 2024), https:\/\/hal.science\/hal-05039081"},{"key":"11_CR16","unstructured":"Merriam-Webster, I.: Merriam-webster\u2019s medical dictionary. Merriam-Webster (1995)"},{"issue":"6","key":"11_CR17","doi-asserted-by":"publisher","first-page":"4228","DOI":"10.1007\/s00330-023-09526-y","volume":"33","author":"S Nowak","year":"2023","unstructured":"Nowak, S., Biesner, D., Layer, Y., Theis, M., Schneider, H., Block, W., Wulff, B., Attenberger, U., Sifa, R., Sprinkart, A.: Transformer-based structuring of free-text radiology report databases. Eur. Radiol. 33(6), 4228\u20134236 (2023)","journal-title":"Eur. Radiol."},{"issue":"5","key":"11_CR18","doi-asserted-by":"publisher","first-page":"2895","DOI":"10.1007\/s00330-023-10373-0","volume":"34","author":"S Nowak","year":"2024","unstructured":"Nowak, S., Schneider, H., Layer, Y.C., Theis, M., Biesner, D., Block, W., Wulff, B., Attenberger, U.I., Sifa, R., Sprinkart, A.M.: Development of image-based decision support systems utilizing information extracted from radiological free-text report databases with text-based transformers. Eur. Radiol. 34(5), 2895\u20132904 (2024)","journal-title":"Eur. Radiol."},{"key":"11_CR19","doi-asserted-by":"publisher","unstructured":"Nowak, S., Wulff, B., Layer, Y.C., Theis, M., Isaak, A., Salam, B., Block, W., Kuetting, D., Pieper, C.C., Luetkens, J.A., Attenberger, U., Sprinkart, A.M.: Privacy-ensuring open-weights large language models are competitive with closed-weights gpt-4o in extracting chest radiography findings from free-text reports. Radiology 314(1), e240895 (2025). https:\/\/doi.org\/10.1148\/radiol.240895, https:\/\/doi.org\/10.1148\/radiol.240895, pMID: 39807977","DOI":"10.1148\/radiol.240895"},{"key":"11_CR20","unstructured":"NuMind AI: Nuextract-2.0-8b. https:\/\/huggingface.co\/numind\/NuExtract-2.0-8B (2024), accessed: 2025-06-17"},{"key":"11_CR21","unstructured":"OpenAI: Introducing gpt-4.1 in the api. https:\/\/openai.com\/index\/introducing-gpt-4-1\/ (1 2025), accessed: 2025-06-17"},{"issue":"2","key":"11_CR22","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/s00247-022-05495-4","volume":"53","author":"G Papaioannou","year":"2023","unstructured":"Papaioannou, G., Caro-Dom\u00ednguez, P., Klein, W.M., Garel, C., Cassart, M.: Indications for magnetic resonance imaging of the fetal body (extra-central nervous system): recommendations from the european society of paediatric radiology fetal task force. Pediatr. Radiol. 53(2), 297\u2013312 (2023)","journal-title":"Pediatr. Radiol."},{"issue":"11","key":"11_CR23","doi-asserted-by":"publisher","first-page":"2105","DOI":"10.1007\/s00247-021-05104-w","volume":"51","author":"G Papaioannou","year":"2021","unstructured":"Papaioannou, G., Klein, W., Cassart, M., Garel, C.: Indications for magnetic resonance imaging of the fetal central nervous system: recommendations from the european society of paediatric radiology fetal task force. Pediatr. Radiol. 51(11), 2105\u20132114 (2021)","journal-title":"Pediatr. Radiol."},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Sato, J., Sugimoto, K., Suzuki, Y., Wataya, T., Kita, K., Nishigaki, D., Tomiyama, M., Hiraoka, Y., Hori, M., Takeda, T., et\u00a0al.: Annotation-free multi-organ anomaly detection in abdominal ct using free-text radiology reports: a multi-centre retrospective study. EBioMedicine 110 (2024)","DOI":"10.1016\/j.ebiom.2024.105463"},{"issue":"10","key":"11_CR25","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1007\/s00247-024-06010-7","volume":"54","author":"C Sofia","year":"2024","unstructured":"Sofia, C., Aertsen, M., Garel, C., Cassart, M.: Standardised and structured reporting in fetal magnetic resonance imaging: recommendations from the fetal task force of the european society of paediatric radiology. Pediatr. Radiol. 54(10), 1566\u20131578 (2024)","journal-title":"Pediatr. Radiol."},{"key":"11_CR26","unstructured":"Team, Q.: Qwen3 technical report (2025), https:\/\/arxiv.org\/abs\/2505.09388"},{"key":"11_CR27","unstructured":"Zhang, S., Dong, L., Li, X., Zhang, S., Sun, X., Wang, S., Li, J., Hu, R., Zhang, T., Wu, F., Wang, G.: Instruction tuning for large language models: A survey (2024), https:\/\/arxiv.org\/abs\/2308.10792"},{"key":"11_CR28","doi-asserted-by":"publisher","unstructured":"Zheng, Y., Zhang, R., Zhang, J., Ye, Y., Luo, Z.: LlamaFactory: Unified efficient fine-tuning of 100+ language models. In: Cao, Y., Feng, Y., Xiong, D. (eds.) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics. pp. 400\u2013410. Bangkok, Thailand (Aug 2024). https:\/\/doi.org\/10.18653\/v1\/2024.acl-demos.38","DOI":"10.18653\/v1\/2024.acl-demos.38"}],"container-title":["Lecture Notes in Computer Science","Perinatal, Preterm and Paediatric Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05997-0_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T07:23:45Z","timestamp":1758525825000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05997-0_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,23]]},"ISBN":["9783032059963","9783032059970"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05997-0_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,23]]},"assertion":[{"value":"23 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PIPPI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Preterm, Perinatal and Paediatric Image Analysis","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":"27 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":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pippi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pippiworkshop.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}