{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:03:57Z","timestamp":1766268237212,"version":"3.37.3"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T00:00:00Z","timestamp":1677456000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T00:00:00Z","timestamp":1677456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Progra","award":["2016YFC01004608"],"award-info":[{"award-number":["2016YFC01004608"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1732119"],"award-info":[{"award-number":["U1732119"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Jiao Tong University Medical Engineering Cross Research Funds","award":["YG2021ZD05"],"award-info":[{"award-number":["YG2021ZD05"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2021YFF0703702"],"award-info":[{"award-number":["2021YFF0703702"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s11517-023-02805-2","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T11:03:48Z","timestamp":1677495828000},"page":"1661-1674","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Automatic vertebral fracture and three-column injury diagnosis with fracture visualization by a multi-scale attention-guided network"],"prefix":"10.1007","volume":"61","author":[{"given":"Shunan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziqi","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duan","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6444-6894","authenticated-orcid":false,"given":"Jianqi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,27]]},"reference":[{"key":"2805_CR1","unstructured":"Whitney E, Alastra AJ (2022) Vertebral fracture. In: StatPearls [Internet]. Treasure Island (FL): Stat Pearls Publishing"},{"issue":"4","key":"2805_CR2","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1007\/s00330-013-3089-2","volume":"24","author":"T Baum","year":"2014","unstructured":"Baum T, Bauer JS, Klinder T, Dobritz M, Rummeny EJ, No\u00ebl PB, Lorenz C (2014) Automatic detection of osteoporotic vertebral fractures in routine thoracic and abdominal MDCT. Eur Radiol 24(4):872\u2013880. https:\/\/doi.org\/10.1007\/s00330-013-3089-2","journal-title":"Eur Radiol"},{"key":"2805_CR3","doi-asserted-by":"publisher","unstructured":"Bar A, Wolf L, Bergman Amitai O, Toledano E, Elnekave E (2017) Compression fractures detection on CT. In: Medical imaging 2017: computer-aided diagnosis. SPIE 10134:1036\u20131043. https:\/\/doi.org\/10.1117\/12.2249635","DOI":"10.1117\/12.2249635"},{"key":"2805_CR4","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.compbiomed.2018.05.011","volume":"98","author":"N Tomita","year":"2018","unstructured":"Tomita N, Cheung YY, Hassanpour S (2018) Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput Biol Med 98:8\u201315. https:\/\/doi.org\/10.1016\/j.compbiomed.2018.05.011","journal-title":"Comput Biol Med"},{"key":"2805_CR5","doi-asserted-by":"publisher","unstructured":"Iyer S, Sowmya A, Blair A, White C, Dawes L, Moses D (2020)\u00a0A novel approach to vertebral compression fracture detection using imitation learning and patch based convolutional neural network. In:\u00a02020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, Iowa, USA, pp 726\u2013730. https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098714","DOI":"10.1109\/ISBI45749.2020.9098714"},{"issue":"3","key":"2805_CR6","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1148\/radiol.2017162100","volume":"284","author":"JE Burns","year":"2017","unstructured":"Burns JE, Yao J, Summers RM (2017) Vertebral body compression fractures and bone density: automated detection and classification on CT images. Radiology 284(3):788\u2013797. https:\/\/doi.org\/10.1148\/radiol.2017162100","journal-title":"Radiology"},{"issue":"1","key":"2805_CR7","doi-asserted-by":"publisher","first-page":"e210015","DOI":"10.1148\/ryai.2021210015","volume":"4","author":"A Suri","year":"2021","unstructured":"Suri A, Jones BC, Ng G, Anabaraonye N, Beyrer P, Domi A, Choi G, Tang S, Terry A, Leichner T, Fathali I, Bastin N, Chesnais H, Taratuta E, Kneeland BJ, Chamith SR (2021) Vertebral deformity measurements at MRI, CT, and radiography using deep learning. Radiology 4(1):e210015. https:\/\/doi.org\/10.1148\/ryai.2021210015","journal-title":"Radiology"},{"issue":"2","key":"2805_CR8","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1080\/17453674.2019.1711323","volume":"91","author":"PH Kalmet","year":"2020","unstructured":"Kalmet PH, Sanduleanu S, Primakov S, Wu G, Jochems A, Refaee T, Ibrahim A, Hulst LV, Lambin P, Poeze M (2020) Deep learning in fracture detection: a narrative review. Acta Orthop 91(2):215\u2013220. https:\/\/doi.org\/10.1080\/17453674.2019.1711323","journal-title":"Acta Orthop"},{"key":"2805_CR9","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.future.2018.03.023","volume":"85","author":"U Raghavendra","year":"2018","unstructured":"Raghavendra U, Bhat NS, Gudigar A, Acharya UR (2018) Automated system for the detection of thoracolumbar fractures using a CNN architecture. Futur Gener Comput Syst 85:184\u2013189. https:\/\/doi.org\/10.1016\/j.future.2018.03.023","journal-title":"Futur Gener Comput Syst"},{"key":"2805_CR10","doi-asserted-by":"publisher","unstructured":"Sha G, Wu J, Yu B (2020)\u00a0Detection of spinal fracture lesions based on improved faster-RCNN. In: 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS). IEEE, Dalian, China, pp 29\u201332. https:\/\/doi.org\/10.1109\/ICAIIS49377.2020.9194863","DOI":"10.1109\/ICAIIS49377.2020.9194863"},{"key":"2805_CR11","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1097\/00003086-198410000-00009","volume":"189","author":"RL Ferguson","year":"1984","unstructured":"Ferguson RL, Allen B Jr (1984) A mechanistic classification of thoracolumbar spine fractures. Clin Orthop Relat Res 189:77\u201388","journal-title":"Clin Orthop Relat Res"},{"issue":"8","key":"2805_CR12","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1097\/00007632-198311000-00003","volume":"8","author":"F Denis","year":"1983","unstructured":"Denis F (1983) The three column spine and its significance in the classification of acute thoracolumbar spinal injuries. Spine 8(8):817\u201331. https:\/\/doi.org\/10.1097\/00007632-198311000-00003","journal-title":"Spine"},{"key":"2805_CR13","doi-asserted-by":"publisher","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proc IEEE Conf Comput Vis Pattern Recognit. IEEE, pp 2921\u20132929. https:\/\/doi.org\/10.48550\/arXiv.1512.04150","DOI":"10.48550\/arXiv.1512.04150"},{"key":"2805_CR14","doi-asserted-by":"publisher","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proc IEEE Int Conf Comput Vision. pp 618\u2013626. https:\/\/doi.org\/10.48550\/arXiv.1802.10171","DOI":"10.48550\/arXiv.1802.10171"},{"key":"2805_CR15","doi-asserted-by":"publisher","unstructured":"Chattopadhyay A, Sarkar A, Howlader P, Balasubramanian VN (2018) Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE,\u00a0Lake Tahoe, NV, USA, pp 839\u2013847. https:\/\/doi.org\/10.1109\/WACV.2018.00097","DOI":"10.1109\/WACV.2018.00097"},{"key":"2805_CR16","doi-asserted-by":"publisher","unstructured":"Li K, Wu Z, Peng K-C, Ernst J, Fu Y (2018) Tell me where to look: guided attention inference network. In: Proc IEEE Conf Comput Vis Pattern Recognit.\u00a0 pp 9215-9223. https:\/\/doi.org\/10.48550\/arXiv.1802.10171","DOI":"10.48550\/arXiv.1802.10171"},{"issue":"7","key":"2805_CR17","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1109\/TMI.2022.3146973","volume":"41","author":"S Wang","year":"2022","unstructured":"Wang S, Ouyang X, Liu T, Wang Q, Shen D (2022) Follow my eye: using gaze to supervise computer-aided diagnosis. IEEE Trans Med Imaging 41(7):1688\u20131698. https:\/\/doi.org\/10.1109\/TMI.2022.3146973","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"2805_CR18","doi-asserted-by":"publisher","first-page":"2698","DOI":"10.1109\/TMI.2020.3042773","volume":"40","author":"X Ouyang","year":"2020","unstructured":"Ouyang X, Karanam S, Wu Z, Chen T, Huo J, Zhou XS, Wang Q, Cheng JZ (2020) Learning hierarchical attention for weakly-supervised chest X-ray abnormality localization and diagnosis. IEEE Trans Med Imaging 40(10):2698\u20132710. https:\/\/doi.org\/10.1109\/TMI.2020.3042773","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"2805_CR19","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1109\/MPUL.2017.2701493","volume":"8","author":"PA Yushkevich","year":"2017","unstructured":"Yushkevich PA, Gerig G (2017) ITK-SNAP: an intractive medical image segmentation tool to meet the need for expert-guided segmentation of complex medical images. IEEE Pulse 8(4):54\u201357. https:\/\/doi.org\/10.1109\/MPUL.2017.2701493","journal-title":"IEEE Pulse"},{"key":"2805_CR20","doi-asserted-by":"publisher","unstructured":"Urschler M, Bischof H, \u0160tern D, Payer C (2020) Coarse to fine vertebrae localization and segmentation with spatial configuration-net and U-Net. In: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. pp 124\u2013133. https:\/\/doi.org\/10.5220\/0008975201240133","DOI":"10.5220\/0008975201240133"},{"key":"2805_CR21","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.media.2019.03.007","volume":"54","author":"C Payer","year":"2019","unstructured":"Payer C, \u0160tern D, Bischof H, Urschler M (2019) Integrating spatial configuration into heatmap regression based CNNs for landmark localization. Med Image Anal 54:207\u2013219. https:\/\/doi.org\/10.1016\/j.media.2019.03.007","journal-title":"Med Image Anal"},{"key":"2805_CR22","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc IEEE Conf Comput Vis Pattern Recognit. pp 770\u2013778. https:\/\/doi.org\/10.48550\/arXiv.1512.03385","DOI":"10.48550\/arXiv.1512.03385"},{"key":"2805_CR23","doi-asserted-by":"publisher","unstructured":"Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proc Eur Conf Comput Vision (ECCV). pp\u00a0 3\u201319. https:\/\/doi.org\/10.48550\/arXiv.1807.06521","DOI":"10.48550\/arXiv.1807.06521"},{"key":"2805_CR24","doi-asserted-by":"publisher","unstructured":"Kingma D, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980. https:\/\/doi.org\/10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"issue":"2","key":"2805_CR25","doi-asserted-by":"publisher","first-page":"01","DOI":"10.5121\/ijdkp.2015.5201","volume":"5","author":"M Hossin","year":"2015","unstructured":"Hossin M, Sulaiman MN (2015) A review on evaluation metrics for data classification evaluations. Int J Data Mining Knowl Manag Proc 5(2):01\u201311","journal-title":"Int J Data Mining Knowl Manag Proc"},{"key":"2805_CR26","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556. https:\/\/doi.org\/10.48550\/arXiv.1409.1556","DOI":"10.48550\/arXiv.1409.1556"},{"key":"2805_CR27","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proc IEEE Conf Comput Vis Pattern Recognit. pp 1\u20139. https:\/\/doi.org\/10.48550\/arXiv.1409.4842","DOI":"10.48550\/arXiv.1409.4842"},{"key":"2805_CR28","doi-asserted-by":"publisher","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proc IEEE Conf Comput Vis Pattern Recognit. pp 2818\u20132826. https:\/\/doi.org\/10.48550\/arXiv.1512.00567","DOI":"10.48550\/arXiv.1512.00567"},{"key":"2805_CR29","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proc IEEE Conf Comput Vis Pattern Recognit. pp 4700\u20134708. https:\/\/doi.org\/10.48550\/arXiv.1608.06993","DOI":"10.48550\/arXiv.1608.06993"},{"key":"2805_CR30","doi-asserted-by":"publisher","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proc IEEE Conf Comput Vis Pattern Recognit.pp 1492\u20131500. https:\/\/doi.org\/10.48550\/arXiv.1611.05431","DOI":"10.48550\/arXiv.1611.05431"},{"key":"2805_CR31","doi-asserted-by":"publisher","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proc IEEE Conf Comput Vis Pattern Recognit. pp 7132\u20137141. https:\/\/doi.org\/10.48550\/arXiv.1709.01507","DOI":"10.48550\/arXiv.1709.01507"},{"key":"2805_CR32","doi-asserted-by":"publisher","unstructured":"Park J, Woo S, Lee JY, Kweon IS (2018) Bam: bottleneck attention module. arXiv preprint arXiv:1807.06514. https:\/\/doi.org\/10.48550\/arXiv.1807.06514","DOI":"10.48550\/arXiv.1807.06514"},{"issue":"12","key":"2805_CR33","doi-asserted-by":"publisher","first-page":"2914","DOI":"10.1109\/TMI.2019.2918096","volume":"38","author":"S Pereira","year":"2019","unstructured":"Pereira S, Pinto A, Amorim J, Ribeiro A, Alves V, Silva CA (2019) Adaptive feature recombination and recalibration for semantic segmentation with fully convolutional networks. IEEE Trans Med Imaging 38(12):2914\u20132925. https:\/\/doi.org\/10.1109\/TMI.2019.2918096","journal-title":"IEEE Trans Med Imaging"},{"key":"2805_CR34","doi-asserted-by":"publisher","unstructured":"Zhang B, Qi S, Wu Y, Pan X, Yao Y, Qian W, Guan Y (2022). Multi-scale segmentation squeeze-and-excitation UNet with conditional random field for segmenting lung tumor from CT images. Comput Methods Prog Biomed 106946.https:\/\/doi.org\/10.1016\/j.cmpb.2022.106946","DOI":"10.1016\/j.cmpb.2022.106946"},{"key":"2805_CR35","doi-asserted-by":"publisher","unstructured":"Zhang Y, Kang B, Hooi B, Yan S, Feng J (2021) Deep long-tailed learning: a survey. arXiv preprint arXiv:2110.04596. https:\/\/doi.org\/10.48550\/arXiv.2110.04596","DOI":"10.48550\/arXiv.2110.04596"},{"key":"2805_CR36","doi-asserted-by":"publisher","first-page":"107965","DOI":"10.1016\/j.patcog.2021.107965","volume":"118","author":"AN Tarekegn","year":"2021","unstructured":"Tarekegn AN, Giacobini M, Michalak K (2021) A review of methods for imbalanced multi-label classification. Pattern Recognit 118:107965. https:\/\/doi.org\/10.1016\/j.patcog.2021.107965","journal-title":"Pattern Recognit"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02805-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02805-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02805-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T01:03:37Z","timestamp":1687136617000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02805-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,27]]},"references-count":36,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["2805"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02805-2","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2023,2,27]]},"assertion":[{"value":"11 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Ethics Committee of Shanghai Sixth People\u2019s Hospital, and the informed consent was waived.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}