{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:13:20Z","timestamp":1778804000217,"version":"3.51.4"},"reference-count":56,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12126603"],"award-info":[{"award-number":["12126603"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82471928"],"award-info":[{"award-number":["82471928"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81974275"],"award-info":[{"award-number":["81974275"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.media.2026.104001","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:13:22Z","timestamp":1771892002000},"page":"104001","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Two -stage contrastive learning framework for vertebral compression fracture screening in frontal chest X-ray"],"prefix":"10.1016","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1017-7297","authenticated-orcid":false,"given":"Junzhang","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4146-9748","authenticated-orcid":false,"given":"Qianyi","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9649-6107","authenticated-orcid":false,"given":"Yi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7366-8284","authenticated-orcid":false,"given":"Yanggang","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9551-093X","authenticated-orcid":false,"given":"Xiongfeng","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3544-2552","authenticated-orcid":false,"given":"Mianwen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4789-7324","authenticated-orcid":false,"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8647-0596","authenticated-orcid":false,"given":"Qianjin","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.media.2026.104001_bib0001","series-title":"2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","first-page":"723","article-title":"Classification of vertebral compression fractures in magnetic resonance images using spectral and fractal analysis","author":"Azevedo-Marques","year":"2015"},{"key":"10.1016\/j.media.2026.104001_bib0002","series-title":"Medical Imaging 2017: Computer-Aided Diagnosis","first-page":"1036","article-title":"Compression fractures detection on CT","author":"Bar","year":"2017"},{"key":"10.1016\/j.media.2026.104001_bib0003","doi-asserted-by":"crossref","unstructured":"Bromiley, P. A., Kariki, E. P., Adams, J. E., Cootes, T. F., 2018. Classification of osteoporotic vertebral fractures using shape and appearance modelling. In: Computational Methods and Clinical Applications in Musculoskeletal Imaging: 5th International Workshop, MSKI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, pp. 133\u2013147. Revised Selected Papers 5. Springer.","DOI":"10.1007\/978-3-319-74113-0_12"},{"issue":"3","key":"10.1016\/j.media.2026.104001_bib0004","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1148\/radiol.2017162100","article-title":"Vertebral body compression fractures and bone density: automated detection and classification on CT images","volume":"284","author":"Burns","year":"2017","journal-title":"Radiology"},{"issue":"10","key":"10.1016\/j.media.2026.104001_bib0005","first-page":"1512","article-title":"Using an ensemble of segmentation methods to detect vertebral bodies on radiographs","volume":"45","author":"Chang","year":"2024","journal-title":"Am. J. Neuroradiol."},{"key":"10.1016\/j.media.2026.104001_bib0006","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, Y., Zheng, K., Li, W., Cheng, C.-T., Harrison, A. P., Xiao, J., Hager, G. D., Lu, L., Liao, C.-H., Miao, S., 2020. Anatomy-aware siamese network: exploiting semantic asymmetry for accurate pelvic fracture detection in X-ray images. arXiv: 2007.01464[cs].","DOI":"10.1007\/978-3-030-58592-1_15"},{"key":"10.1016\/j.media.2026.104001_bib0007","series-title":"Medical Image Computing and Computer Assisted Intervention-MICCAI 2020: 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part VI 23","first-page":"743","article-title":"3D convolutional sequence to sequence model for vertebral compression fractures identification in CT","author":"Chettrit","year":"2020"},{"issue":"2","key":"10.1016\/j.media.2026.104001_bib0008","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1002\/jbmr.5650070214","article-title":"Incidence of clinically diagnosed vertebral fractures: a population-based study in rochester, minnesota, 1985-1989","volume":"7","author":"Cooper","year":"1992","journal-title":"J. Bone Miner. Res."},{"issue":"9","key":"10.1016\/j.media.2026.104001_bib0009","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1016\/j.jacr.2023.11.029","article-title":"Cost-effectiveness of artificial intelligence\u2013based opportunistic compression fracture screening of existing radiographs","volume":"21","author":"Curl","year":"2024","journal-title":"J. Am. Coll. Radiol."},{"issue":"4","key":"10.1016\/j.media.2026.104001_bib0010","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1359\/JBMR.041214","article-title":"Underdiagnosis of vertebral fractures is a worldwide problem: the IMPACT study","volume":"20","author":"Delmas","year":"2005","journal-title":"J. Bone Miner. Res."},{"key":"10.1016\/j.media.2026.104001_bib0011","series-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","first-page":"1436","article-title":"Deep learning for automatic pneumonia detection","author":"Gabruseva","year":"2020"},{"key":"10.1016\/j.media.2026.104001_bib0012","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1007\/s001980070078","article-title":"Recognition of vertebral fracture in a clinical setting","volume":"11","author":"Gehlbach","year":"2000","journal-title":"Osteoporos. Int."},{"issue":"9","key":"10.1016\/j.media.2026.104001_bib0013","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1002\/jbmr.5650080915","article-title":"Vertebral fracture assessment using a semiquantitative technique","volume":"8","author":"Genant","year":"1993","journal-title":"J. Bone Miner. Res."},{"key":"10.1016\/j.media.2026.104001_bib0014","unstructured":"Goo\u00dfen, A., Deshpande, H., Harder, T., Schwab, E., Baltruschat, I., Mabotuwana, T., Cross, N., Saalbach, A., 2019. Deep learning for pneumothorax detection and localization in chest radiographs. arXiv: 1907.07324. [cs, eess]."},{"key":"10.1016\/j.media.2026.104001_bib0015","series-title":"RoFo-Fortschritte Auf Dem Gebiet Der ROntgenstrahlen Und Der Bildgebenden Verfahren","first-page":"1074","article-title":"Impact of specific training in detecting osteoporotic vertebral fractures on routine chest radiographs","volume":"Vol. 185","author":"Gruber","year":"2013"},{"key":"10.1016\/j.media.2026.104001_bib0016","series-title":"Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19 ns\u201322, 2018, Proceedings 23","first-page":"757","article-title":"Learning to recognize abnormalities in chest X-rays with location-aware dense networks","author":"Guendel","year":"2019"},{"key":"10.1016\/j.media.2026.104001_bib0017","doi-asserted-by":"crossref","unstructured":"Han, Y., Chen, C., Tewfik, A., Glicksberg, B., Ding, Y., Peng, Y., Wang, Z., 2022. Knowledge-augmented contrastive learning for abnormality classification and localization in chest X-rays with radiomics using a feedback loop. arXiv: 2104.04968. [cs].","DOI":"10.1109\/WACV51458.2022.00185"},{"key":"10.1016\/j.media.2026.104001_bib0018","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"590","article-title":"CheXPert: a large chest radiograph dataset with uncertainty labels and expert comparison","volume":"Vol. 33","author":"Irvin","year":"2019"},{"issue":"2","key":"10.1016\/j.media.2026.104001_bib0019","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"Nnu-net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"issue":"4","key":"10.1016\/j.media.2026.104001_bib0020","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1007\/s12559-022-10032-4","article-title":"MXT: a new variant of pyramid vision transformer for multi-label chest X-ray image classification","volume":"14","author":"Jiang","year":"2022","journal-title":"Cognit. Comput."},{"issue":"1","key":"10.1016\/j.media.2026.104001_bib0021","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1038\/s41597-019-0322-0","article-title":"Mimic-CXR, a de-identified publicly available database of chest radiographs with free-text reports","volume":"6","author":"Johnson","year":"2019","journal-title":"Sci. Data"},{"issue":"2","key":"10.1016\/j.media.2026.104001_bib0022","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1080\/17453674.2019.1711323","article-title":"Deep learning in fracture detection: a narrative review","volume":"91","author":"Kalmet","year":"2020","journal-title":"Acta Orthop."},{"key":"10.1016\/j.media.2026.104001_bib0023","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1007\/s00198-019-04962-3","article-title":"Prevalent vertebral fractures and minor vertebral deformities analyzed by vertebral fracture assessment (VFA) increases the risk of incident fractures in postmenopausal women: the FRODOS study","volume":"30","author":"Kanterewicz","year":"2019","journal-title":"Osteoporos. Int."},{"issue":"5","key":"10.1016\/j.media.2026.104001_bib0024","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.crad.2017.11.015","article-title":"Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks","volume":"73","author":"Kim","year":"2018","journal-title":"Clin. Radiol."},{"issue":"2","key":"10.1016\/j.media.2026.104001_bib0025","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2214\/ajr.182.2.1820297","article-title":"Underreporting of vertebral fractures on routine chest radiography","volume":"182","author":"Kim","year":"2004","journal-title":"Am. J. Roentgenol."},{"key":"10.1016\/j.media.2026.104001_bib0026","doi-asserted-by":"crossref","unstructured":"Lei, Y., Li, Z., Shen, Y., Zhang, J., Shan, H., 2023. CLIP-Lung: textual knowledge-guided lung nodule malignancy prediction. Vol. 14226, pp. 403\u2013412. arXiv: 2304.08013. [cs]. 10.1007\/978-3-031-43990-2\\_38.","DOI":"10.1007\/978-3-031-43990-2_38"},{"key":"10.1016\/j.media.2026.104001_bib0027","series-title":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1371","article-title":"Self-knowledge distillation based self-supervised learning for covid-19\u202fdetection from chest X-ray images","author":"Li","year":"2022"},{"issue":"7","key":"10.1016\/j.media.2026.104001_bib0028","doi-asserted-by":"crossref","first-page":"3139","DOI":"10.1109\/JBHI.2022.3152267","article-title":"CNN attention guidance for improved orthopedics radiographic fracture classification","volume":"26","author":"Liao","year":"2022","journal-title":"IEEE J. Biomed. Health Inf."},{"issue":"3","key":"10.1016\/j.media.2026.104001_bib0029","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1001\/jama.285.3.320","article-title":"Risk of new vertebral fracture in the year following a fracture","volume":"285","author":"Lindsay","year":"2001","journal-title":"JAMA"},{"issue":"45","key":"10.1016\/j.media.2026.104001_bib0030","doi-asserted-by":"crossref","first-page":"11591","DOI":"10.1073\/pnas.1806905115","article-title":"Deep neural network improves fracture detection by clinicians","volume":"115","author":"Lindsey","year":"2018","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"11","key":"10.1016\/j.media.2026.104001_bib0031","first-page":"830","article-title":"An analysis of medical X-ray examination frequency in ten hospitals in Tianjin, China","volume":"36","author":"Liu","year":"2018","journal-title":"Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi"},{"issue":"2","key":"10.1016\/j.media.2026.104001_bib0032","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1148\/radiol.2020192256","article-title":"Patient exposure from radiologic and nuclear medicine procedures in the united states: procedure volume and effective dose for the period 2006\u20132016","volume":"295","author":"Mettler","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.media.2026.104001_bib0033","doi-asserted-by":"crossref","unstructured":"Nicolaes, J., Raeymaeckers, S., Robben, D., Wilms, G., Vandermeulen, D., Libanati, C., Debois, M., 2019. Detection of vertebral fractures in CT using 3D convolutional neural networks. arXiv: 1911.01816[cs, eess].","DOI":"10.1016\/j.bonr.2020.100539"},{"key":"10.1016\/j.media.2026.104001_bib0034","doi-asserted-by":"crossref","unstructured":"Niu, C., Dasegowda, G., Yan, P., Kalra, M. K., Wang, G., 2022. X-ray dissectography improves lung nodule detection. arXiv: 2203.13118[cs, eess].","DOI":"10.1117\/12.2637782"},{"key":"10.1016\/j.media.2026.104001_bib0035","series-title":"2020 IEEE International Conference on E-Health Networking, Application & Services (HEALTHCOM)","first-page":"1","article-title":"Multi-view weighted feature fusion using CNN for pneumonia detection on chest X-rays","author":"Peng","year":"2021"},{"key":"10.1016\/j.media.2026.104001_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101823","article-title":"Re-identification and growth detection of pulmonary nodules without image registration using 3D siamese neural networks","volume":"67","author":"Rafael-Palou","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104001_bib0037","author":"Rajpurkar"},{"issue":"1","key":"10.1016\/j.media.2026.104001_bib0038","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-93017-x","article-title":"A deep learning algorithm for automated measurement of vertebral body compression from X-ray images","volume":"11","author":"Seo","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.media.2026.104001_bib0039","doi-asserted-by":"crossref","unstructured":"Shen, L., Gao, C., Hu, S., Kang, D., Zhang, Z., Xia, D., Xu, Y., Xiang, S., Zhu, Q., Xu, G., et al.,. Using artificial intelligence to diagnose osteoporotic vertebral fractures on plain radiographs., 2023, 38. 4879, 1278\u20131287. 10.1002\/jbmr.","DOI":"10.1002\/jbmr.4879"},{"key":"10.1016\/j.media.2026.104001_bib0040","doi-asserted-by":"crossref","first-page":"S27","DOI":"10.1016\/8756-3282(92)90193-Z","article-title":"The clinical consequences of vertebral compression fracture","volume":"13","author":"Silverman","year":"1992","journal-title":"Bone"},{"issue":"5","key":"10.1016\/j.media.2026.104001_bib0041","article-title":"Fracture risk in men and women with vertebral fractures identified opportunistically on routine computed tomography scans and not treated for osteoporosis: an observational cohort study","volume":"7","author":"Skj\u00f8dt","year":"2023","journal-title":"J. Bone Mineral Res. Plus"},{"issue":"4","key":"10.1016\/j.media.2026.104001_bib0042","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1007\/s00198-024-07017-4","article-title":"Opportunistically identifiable vertebral fractures on routine radiological imaging predict mortality: observational cohort study","volume":"35","author":"Skj\u00f8dt","year":"2024","journal-title":"Osteoporos. Int."},{"key":"10.1016\/j.media.2026.104001_bib0043","unstructured":"Tai, Y., Fang, Y.-W., Su, F.-Y., Chiang, J.-H., A deep learning based workflow for detection of lung nodules with chest radiograph. arXiv preprint arXiv: 2112.10184."},{"issue":"4","key":"10.1016\/j.media.2026.104001_bib0044","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.3390\/app10041507","article-title":"X-ray bone fracture classification using deep learning: a baseline for designing a reliable approach","volume":"10","author":"Tanzi","year":"2020","journal-title":"Appl. Sci."},{"key":"10.1016\/j.media.2026.104001_bib0045","unstructured":"Taslimi, S., Taslimi, S., Fathi, N., Salehi, M., Rohban, M. H., 2022. SwinCheX: multi-label classification on chest X-ray images with transformers. arXiv: 2206.04246. [cs]."},{"key":"10.1016\/j.media.2026.104001_bib0046","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compbiomed.2018.05.011","article-title":"Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans","volume":"98","author":"Tomita","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.media.2026.104001_bib0047","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.jare.2022.08.021","article-title":"A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images","volume":"48","author":"Ukwuoma","year":"2023","journal-title":"J. Adv. Res."},{"key":"10.1016\/j.media.2026.104001_bib0048","unstructured":"Vu, Y. N. T., Wang, R., Balachandar, N., Liu, C., Ng, A. Y., Rajpurkar, P., 2021. MedAug: contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation. arXiv: 2102.10663[cs, eess]."},{"key":"10.1016\/j.media.2026.104001_bib0049","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101846","article-title":"Triple attention learning for classification of 14 thoracic diseases using chest radiography","volume":"67","author":"Wang","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104001_bib0050","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2097","article-title":"ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases","author":"Wang","year":"2017"},{"issue":"8","key":"10.1016\/j.media.2026.104001_bib0051","doi-asserted-by":"crossref","first-page":"4259","DOI":"10.21037\/qims-22-433","article-title":"A software program for automated compressive vertebral fracture detection on elderly women\u2019s lateral chest radiograph: ofeye 1.0","volume":"12","author":"Xiao","year":"2022","journal-title":"Quant. Imaging Med. Surg."},{"issue":"9","key":"10.1016\/j.media.2026.104001_bib0052","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1109\/TMI.2023.3264298","article-title":"Two-stage structure-focused contrastive learning for automatic identification and localization of complex pelvic fractures","volume":"42","author":"Zeng","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104001_bib0053","series-title":"Machine Learning for Healthcare Conference","first-page":"2","article-title":"Contrastive learning of medical visual representations from paired images and text","author":"Zhang","year":"2022"},{"key":"10.1016\/j.media.2026.104001_bib0054","unstructured":"Zhao, G., Fang, C., Li, G., Jiao, L., Yu, Y., 2020. Contralaterally enhanced networks for thoracic disease detection. arXiv: 2010.04483[cs, eess]."},{"issue":"4","key":"10.1016\/j.media.2026.104001_bib0055","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1109\/TMI.2021.3049498","article-title":"Contrast-attentive thoracic disease recognition with dual-weighting graph reasoning","volume":"40","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104001_bib0056","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2022.102137","article-title":"PCAN: pixel-wise classification and attention network for thoracic disease classification and weakly supervised localization","volume":"102","author":"Zhu","year":"2022","journal-title":"Comput. Med. Imaging Graph."}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526000708?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526000708?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:05:49Z","timestamp":1778803549000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841526000708"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":56,"alternative-id":["S1361841526000708"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2026.104001","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Two -stage contrastive learning framework for vertebral compression fracture screening in frontal chest X-ray","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2026.104001","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"104001"}}