{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T13:43:21Z","timestamp":1774705401405,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439865","type":"print"},{"value":"9783031439872","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43987-2_9","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:07:48Z","timestamp":1696115268000},"page":"85-94","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multi-modality Contrastive Learning for\u00a0Sarcopenia Screening from\u00a0Hip X-rays and\u00a0Clinical Information"],"prefix":"10.1007","author":[{"given":"Qiangguo","family":"Jin","sequence":"first","affiliation":[]},{"given":"Changjiang","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Changming","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Shu-Wei","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yi-Jie","family":"Kuo","sequence":"additional","affiliation":[]},{"given":"Ping","family":"Xuan","sequence":"additional","affiliation":[]},{"given":"Leilei","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Ran","family":"Su","sequence":"additional","affiliation":[]},{"given":"Leyi","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Henry B. L.","family":"Duh","sequence":"additional","affiliation":[]},{"given":"Yu-Pin","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Ackermans, L.L., et al.: Screening, diagnosis and monitoring of sarcopenia: when to use which tool? Clinical Nutrition ESPEN (2022)","DOI":"10.1016\/j.clnesp.2022.01.027"},{"key":"9_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1007\/978-3-030-87240-3_64","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"N Braman","year":"2021","unstructured":"Braman, N., Gordon, J.W.H., Goossens, E.T., Willis, C., Stumpe, M.C., Venkataraman, J.: Deep orthogonal fusion: multimodal prognostic biomarker discovery integrating radiology, pathology, genomic, and clinical data. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 667\u2013677. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_64"},{"issue":"4","key":"9_CR3","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1109\/TMI.2020.3021387","volume":"41","author":"RJ Chen","year":"2020","unstructured":"Chen, R.J., et al.: Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans. Med. Imaging 41(4), 757\u2013770 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR4","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"issue":"1","key":"9_CR5","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1093\/ageing\/afy169","volume":"48","author":"AJ Cruz-Jentoft","year":"2019","unstructured":"Cruz-Jentoft, A.J., et al.: Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 48(1), 16\u201331 (2019)","journal-title":"Age Ageing"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: AutoAugment: learning augmentation strategies from data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 113\u2013123 (2019)","DOI":"10.1109\/CVPR.2019.00020"},{"issue":"2","key":"9_CR7","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1002\/jcsm.12157","volume":"8","author":"RM Dodds","year":"2017","unstructured":"Dodds, R.M., Granic, A., Davies, K., Kirkwood, T.B., Jagger, C., Sayer, A.A.: Prevalence and incidence of sarcopenia in the very old: findings from the Newcastle 85+ study. J. Cachexia, Sarcopenia Muscle 8(2), 229\u2013237 (2017)","journal-title":"J. Cachexia, Sarcopenia Muscle"},{"issue":"23","key":"9_CR8","doi-asserted-by":"publisher","first-page":"5552","DOI":"10.3390\/jcm10235552","volume":"10","author":"S Giovannini","year":"2021","unstructured":"Giovannini, S., et al.: Sarcopenia: diagnosis and management, state of the art and contribution of ultrasound. J. Clin. Med. 10(23), 5552 (2021)","journal-title":"J. Clin. Med."},{"key":"9_CR9","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":"9_CR10","unstructured":"Omeiza, D., Speakman, S., Cintas, C., Weldermariam, K.: Smooth grad-CAM++: an enhanced inference level visualization technique for deep convolutional neural network models. arXiv preprint arXiv:1908.01224 (2019)"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Pang, B.W.J., et al.: Prevalence and associated factors of Sarcopenia in Singaporean adults-the Yishun Study. J. Am. Med. Direct. Assoc. 22(4), e1-885 (2021)","DOI":"10.1016\/j.jamda.2020.05.029"},{"issue":"1","key":"9_CR12","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1002\/jcsm.13144","volume":"14","author":"J Ryu","year":"2022","unstructured":"Ryu, J., Eom, S., Kim, H.C., Kim, C.O., Rhee, Y., You, S.C., Hong, N.: Chest X-ray-based opportunistic screening of sarcopenia using deep learning. J. Cachexia, Sarcopenia Muscle 14(1), 418\u2013428 (2022)","journal-title":"J. Cachexia, Sarcopenia Muscle"},{"issue":"1","key":"9_CR13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40200-016-0284-0","volume":"16","author":"G Shafiee","year":"2017","unstructured":"Shafiee, G., Keshtkar, A., Soltani, A., Ahadi, Z., Larijani, B., Heshmat, R.: Prevalence of sarcopenia in the world: a systematic review and meta-analysis of general population studies. J. Diab. Metab. Disord. 16(1), 1\u201310 (2017)","journal-title":"J. Diab. Metab. Disord."},{"issue":"1","key":"9_CR14","first-page":"3221","volume":"15","author":"L Van Der Maaten","year":"2014","unstructured":"Van Der Maaten, L.: Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15(1), 3221\u20133245 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"9_CR15","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Yan, K., Guo, Y., Liu, B.: Pretp-2l: identification of therapeutic peptides and their types using two-layer ensemble learning framework. Bioinformatics 39(4), btad125 (2023)","DOI":"10.1093\/bioinformatics\/btad125"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Yan, K., Lv, H., Guo, Y., Peng, W., Liu, B.: samppred-gat: prediction of antimicrobial peptide by graph attention network and predicted peptide structure. Bioinformatics 39(1), btac715 (2023)","DOI":"10.1093\/bioinformatics\/btac715"},{"issue":"9","key":"9_CR19","doi-asserted-by":"publisher","first-page":"2092","DOI":"10.1109\/TMI.2019.2893944","volume":"38","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Xie, Y., Xia, Y., Shen, C.: Attention residual learning for skin lesion classification. IEEE Trans. Med. Imaging 38(9), 2092\u20132103 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43987-2_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T15:29:20Z","timestamp":1710170960000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43987-2_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439865","9783031439872"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43987-2_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","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)"}}]}}