{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T09:38:40Z","timestamp":1776850720552,"version":"3.51.2"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051813","type":"print"},{"value":"9783032051820","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"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\/978-3-032-05182-0_22","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:02Z","timestamp":1758153602000},"page":"218-227","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fine-Grained Rib Fracture Diagnosis with Hyperbolic Embeddings: A Detailed Annotation Framework and Multi-label Classification Model"],"prefix":"10.1007","author":[{"given":"Shripad","family":"Pate","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3238-7962","authenticated-orcid":false,"given":"Aiman","family":"Farooq","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6434-1408","authenticated-orcid":false,"given":"Suvrankar","family":"Datta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Musadiq Aadil","family":"Sheikh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atin","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4078-9400","authenticated-orcid":false,"given":"Deepak","family":"Mishra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","first-page":"1584","DOI":"10.1109\/TMM.2023.3263074","volume":"25","author":"Z Cao","year":"2023","unstructured":"Cao, Z., Xu, L., Chen, D.Z., Gao, H., Wu, J.: A robust shape-aware rib fracture detection and segmentation framework with contrastive learning. IEEE Trans. Multimedia 25, 1584\u20131591 (2023)","journal-title":"IEEE Trans. Multimedia"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Castro-Zunti, R., et al.: Ribfracturesys: a gem in the face of acute rib fracture diagnoses. Comput. Med. Imaging Graph. 117, 102429 (2024)","DOI":"10.1016\/j.compmedimag.2024.102429"},{"issue":"1","key":"22_CR3","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1097\/TA.0000000000000867","volume":"80","author":"BC Chapman","year":"2016","unstructured":"Chapman, B.C.: Ribscore: a novel radiographic score based on fracture pattern that predicts pneumonia, respiratory failure, and tracheostomy. J. Trauma Acute Care Surg. 80(1), 95\u2013101 (2016)","journal-title":"J. Trauma Acute Care Surg."},{"key":"22_CR4","unstructured":"Desai, K., Nickel, M., Rajpurohit, T., Johnson, J., Vedantam, S.R.: Hyperbolic image-text representations. In: International Conference on Machine Learning, pp. 7694\u20137731. PMLR (2023)"},{"issue":"2","key":"22_CR5","doi-asserted-by":"publisher","first-page":"e40","DOI":"10.1097\/TA.0000000000002282","volume":"88","author":"JG Edwards","year":"2020","unstructured":"Edwards, J.G.: Taxonomy of multiple rib fractures: results of the chest wall injury society international consensus survey. J. Trauma Acute Care Surg. 88(2), e40\u2013e45 (2020)","journal-title":"J. Trauma Acute Care Surg."},{"issue":"8","key":"22_CR6","doi-asserted-by":"publisher","first-page":"5399","DOI":"10.21037\/jtd-23-1832","volume":"16","author":"AJ Franssen","year":"2024","unstructured":"Franssen, A.J., et al.: Treatment of traumatic rib fractures: an overview of current evidence and future perspectives. J. Thorac. Dis. 16(8), 5399 (2024)","journal-title":"J. Thorac. Dis."},{"key":"22_CR7","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. corr abs\/1512.03385 (2015) (2015)"},{"key":"22_CR8","unstructured":"Huang, K., Altosaar, J., Ranganath, R.: Clinicalbert: Modeling clinical notes and predicting hospital readmission. arXiv preprint arXiv:1904.05342 (2019)"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Jin, L., et\u00a0al.: Deep-learning-assisted detection and segmentation of rib fractures from CT scans: development and validation of fracnet. EBioMedicine 62 (2020)","DOI":"10.1016\/j.ebiom.2020.103106"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Khrulkov, V., Mirvakhabova, L., Ustinova, E., Oseledets, I., Lempitsky, V.: Hyperbolic image embeddings. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 6418\u20136428 (2020)","DOI":"10.1109\/CVPR42600.2020.00645"},{"issue":"1146","key":"22_CR11","doi-asserted-by":"publisher","first-page":"20221006","DOI":"10.1259\/bjr.20221006","volume":"96","author":"N Li","year":"2023","unstructured":"Li, N., et al.: An automatic fresh rib fracture detection and positioning system using deep learning. Br. J. Radiol. 96(1146), 20221006 (2023)","journal-title":"Br. J. Radiol."},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Multi-scale segmentation network for rib fracture classification from CT images. In: Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 12, pp. 546\u2013554. Springer (2021)","DOI":"10.1007\/978-3-030-87589-3_56"},{"key":"22_CR14","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28 (2015)"},{"issue":"6","key":"22_CR15","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"22_CR16","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.1007\/s00330-015-3598-2","volume":"25","author":"H Ringl","year":"2015","unstructured":"Ringl, H.: The ribs unfolded-a CT visualization algorithm for fast detection of rib fractures: effect on sensitivity and specificity in trauma patients. Eur. Radiol. 25, 1865\u20131874 (2015)","journal-title":"Eur. Radiol."},{"key":"22_CR17","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"22_CR18","unstructured":"Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Wu, M., et al.: Development and evaluation of a deep learning algorithm for rib segmentation and fracture detection from multicenter chest CT images. Radiol. Artif. Intell. 3(5), e200248 (2021)","DOI":"10.1148\/ryai.2021200248"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"22_CR21","unstructured":"Yang, J., Ni, B.: Deep rib fracture instance segmentation and classification from CT on the RibFrac challenge. arXiv Preprint (2024)"},{"key":"22_CR22","unstructured":"Yang, J., et\u00a0al.: Deep rib fracture instance segmentation and classification from CT on the ribfrac challenge. arXiv preprint arXiv:2402.09372 (2024)"},{"key":"22_CR23","doi-asserted-by":"publisher","first-page":"3815","DOI":"10.1007\/s00330-020-07418-z","volume":"31","author":"QQ Zhou","year":"2021","unstructured":"Zhou, Q.Q.: Automatic detection and classification of rib fractures based on patients\u2019 CT images and clinical information via convolutional neural network. Eur. Radiol. 31, 3815\u20133825 (2021)","journal-title":"Eur. Radiol."},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Zhu, Z., et al.: Cross-view deformable transformer for non-displaced hip fracture classification from frontal-lateral x-ray pair. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 444\u2013453. Springer (2023)","DOI":"10.1007\/978-3-031-43987-2_43"}],"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\/978-3-032-05182-0_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T08:39:45Z","timestamp":1776847185000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05182-0_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,18]]},"ISBN":["9783032051813","9783032051820"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05182-0_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,18]]},"assertion":[{"value":"18 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":"23 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"}}]}}