{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:52:48Z","timestamp":1786981968029,"version":"build-2736575974"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T00:00:00Z","timestamp":1620432000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T00:00:00Z","timestamp":1620432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["81871508"],"award-info":[{"award-number":["81871508"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773246"],"award-info":[{"award-number":["61773246"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Program of Shandong Province Natural Science Foundation","award":["ZR2018ZB0419"],"award-info":[{"award-number":["ZR2018ZB0419"]}]},{"name":"Taishan Scholar Program of Shandong Province of China","award":["TSHW201502038"],"award-info":[{"award-number":["TSHW201502038"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s11548-021-02391-4","type":"journal-article","created":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T14:02:42Z","timestamp":1620482562000},"page":"979-988","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Multiview multimodal network for breast cancer diagnosis in contrast-enhanced spectral mammography images"],"prefix":"10.1007","volume":"16","author":[{"given":"Jingqi","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanjie","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Zakir Ullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junxia","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanyun","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenxi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenxing","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guocheng","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,5,8]]},"reference":[{"issue":"144459","key":"2391_CR1","first-page":"02","volume":"737","author":"R Farzad","year":"2020","unstructured":"Farzad R, Gordon F, Sahar T, Mahnaz N, Amir A, Soodabeh S (2020) Role of regulatory miRNAS of the pi3K\/AKT signaling pathway in the pathogenesis of breast cancer. Gene 737(144459):02","journal-title":"Gene"},{"issue":"1","key":"2391_CR2","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21654","volume":"71","author":"R Siegel","year":"2021","unstructured":"Siegel R, Miller K, Fuchs H, Jemal A (2021) Cancer statistics. CA A Cancer J Clin 71(1):7\u201333","journal-title":"CA A Cancer J Clin"},{"key":"2391_CR3","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.neucom.2019.01.112","volume":"392","author":"C Hiba","year":"2020","unstructured":"Hiba C, Hamid Z, Omar A (2020) Multi-label transfer learning for the early diagnosis of breast cancer. Neurocomputing 392:168\u2013180","journal-title":"Neurocomputing"},{"issue":"4","key":"2391_CR4","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1007\/s00404-015-3693-2","volume":"292","author":"D Martin","year":"2015","unstructured":"Martin D, Tobias DZ, Wolfram S, Birgit A, Florian K, Werner J, Clarisse D, Willi O, Michael H, Christian M (2015) Dual-energy contrast-enhanced spectral mammography (CESM). Arch Gynecol Obstet 292(4):739\u2013747","journal-title":"Arch Gynecol Obstet"},{"issue":"10","key":"2391_CR5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0222816","volume":"14","author":"H Lisa","year":"2019","unstructured":"Lisa H, Martin D, Christoph J, Elena E, Julia H (2019) Contrast-enhanced spectral mammography with a compact synchrotron source. PLoS ONE 14(10):","journal-title":"PLoS ONE"},{"issue":"8","key":"2391_CR6","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1016\/j.crad.2018.05.005","volume":"73","author":"JJ James","year":"2018","unstructured":"James JJ, Tennant SL (2018) Contrast-enhanced spectral mammography (CESM). Clin Radiol 73(8):715\u2013723","journal-title":"Clin Radiol"},{"issue":"9","key":"2391_CR7","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1016\/j.crad.2013.04.009","volume":"68","author":"MBI Lobbes","year":"2013","unstructured":"Lobbes MBI, Smidt ML, Houwers J, Tjan-Heijnen VC, Wildberger JE (2013) Contrast enhanced mammography: techniques, current results, and potential indications. Clin Radiol 68(9):935\u2013944","journal-title":"Clin Radiol"},{"issue":"4","key":"2391_CR8","doi-asserted-by":"publisher","first-page":"1082","DOI":"10.1007\/s00330-015-3904-z","volume":"26","author":"YC Cheung","year":"2016","unstructured":"Cheung YC, Tsai HP, Lo YF, Ueng SH, Huang PC, Chen SC (2016) Clinical utility of dual-energy contrast-enhanced spectral mammography for breast microcalcifications without associated mass: a preliminary analysis. Eur Radiol 26(4):1082\u20131089","journal-title":"Eur Radiol"},{"issue":"3","key":"2391_CR9","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.ejrad.2017.11.024","volume":"98","author":"BK Patel","year":"2018","unstructured":"Patel BK, Ranjbar S, Wu T, Pockaj BA, Li J, Zhang N, Lobbes M, Zhang B, Mitchell JR (2018) Computer-aided diagnosis of contrast-enhanced spectral mammography: a feasibility study. Eur J Radiol 98(3):207\u2013213","journal-title":"Eur J Radiol"},{"issue":"9","key":"2391_CR10","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1007\/s10439-018-2044-4","volume":"46","author":"D Gopichandh","year":"2018","unstructured":"Gopichandh D, Bhavika P, Faranak A, Morteza H, Jing L, Teresa W, Bin Z (2018) Classification of breast masses using a computer-aided diagnosis scheme of contrast enhanced digital mammograms. Ann Biomed Eng 46(9):1419\u20131431","journal-title":"Ann Biomed Eng"},{"issue":"3","key":"2391_CR11","doi-asserted-by":"publisher","first-page":"R94","DOI":"10.1186\/bcr3210","volume":"14","author":"C Dromain","year":"2012","unstructured":"Dromain C, Thibault F, Diekmann F, Fallenberg EM, Jong RA, Koomen M, Hendrick RE, Tardivon A, Toledano A (2012) Dual-energy contrast-enhanced digital mammography: initial clinical results of a multireader, multicase study. Breast Cancer Res 14(3):R94","journal-title":"Breast Cancer Res"},{"key":"2391_CR12","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.breast.2016.04.008","volume":"28","author":"AS Tagliafico","year":"2016","unstructured":"Tagliafico AS, Bignotti B, Rossi F, Signori A, Sormani MP, Vadora F, Calabrese M, Houssami N (2016) Diagnostic performance of contrast-enhanced spectral mammography: systematic review and meta-analysis. Breast 28:13\u201319","journal-title":"Breast"},{"key":"2391_CR13","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.compmedimag.2018.09.004","volume":"70","author":"G Fei","year":"2018","unstructured":"Fei G, Teresa W, Jing L, Bin Z, Lingxiang R, Desheng S, Bhavika P (2018) SD-CNN: a shallow-deep CNN for improved breast cancer diagnosis. Comput Med Imaging Graph 70:53\u201362","journal-title":"Comput Med Imaging Graph"},{"key":"2391_CR14","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.breast.2019.12.007","volume":"49","author":"I Asmaa","year":"2020","unstructured":"Asmaa I, Paul G, Ronnachai J, Abdelsamea Mohammed M, Mermel Craig H, Po HCC, Rakha Emad A (2020) Artificial intelligence in digital breast pathology: techniques and applications. Breast 49:267\u2013273","journal-title":"Breast"},{"issue":"2","key":"2391_CR15","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.media.2009.12.005","volume":"14","author":"O Arnau","year":"2010","unstructured":"Arnau O, Jordi F, Joan M, Elsa P, Josep P, Denton Erika RE, Reyer Z (2010) A review of automatic mass detection and segmentation in mammographic images. Med Image Anal 14(2):87\u2013110","journal-title":"Med Image Anal"},{"key":"2391_CR16","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.trsl.2017.10.010","volume":"194","author":"R Stephanie","year":"2018","unstructured":"Stephanie R, Hossein A, Kevin S, Johan H (2018) Digital image analysis in breast pathology from image processing techniques to artificial intelligence. Transl Res 194:19\u201335","journal-title":"Transl Res"},{"issue":"5","key":"2391_CR17","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.crad.2019.02.006","volume":"74","author":"EPV Le","year":"2019","unstructured":"Le EPV, Wang Y, Huang Y, Hickman S, Gilbert FJ (2019) Artificial intelligence in breast imaging. Clin Radiol 74(5):357\u2013366","journal-title":"Clin Radiol"},{"key":"2391_CR18","doi-asserted-by":"crossref","unstructured":"Habib G, Kiryati N, Sklair-Levy M, Shalmon A, Neiman O, Weidenfeld R, Yagil Y, Konen E, Mayer A (2020) Automatic breast lesion classification by joint neural analysis of mammography and ultrasound. In: Multimodal learning for clinical decision support and clinical image-based procedures: 10th international workshop, ML-CDS 2020, and 9th international workshop, CLIP 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4\u20138, 2020, Proceedings, vol 12445, pp 125\u2013135. Springer","DOI":"10.1007\/978-3-030-60946-7_13"},{"issue":"1","key":"2391_CR19","first-page":"1","volume":"31","author":"TA Shaikh","year":"2020","unstructured":"Shaikh TA, Rashid A, Sufyan Beg MM (2020) Transfer learning privileged information fuels cad diagnosis of breast cancer. Mach Vis Appl 31(1):1\u201323","journal-title":"Mach Vis Appl"},{"key":"2391_CR20","doi-asserted-by":"crossref","unstructured":"Mateos MJ, Gastelum A, M\u00e1rquez J, Brandan ME (2016). Texture analysis of contrast-enhanced digital mammography (CEDM) images. In: Lecture Notes in Computer Science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 9699. Springer, pp 585\u2013592","DOI":"10.1007\/978-3-319-41546-8_73"},{"issue":"2","key":"2391_CR21","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s11548-018-1876-6","volume":"14","author":"P Shaked","year":"2019","unstructured":"Shaked P, Nahum K, Gali Z-M, Miri S-L, Eli K, Arnaldo M (2019) Classification of contrast-enhanced spectral mammography (CESM) images. Int J Comput Assist Radiol Surg 14(2):249\u2013257","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"3","key":"2391_CR22","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/S0033-8389(01)00017-3","volume":"40","author":"L Laura","year":"2002","unstructured":"Laura L, Menell Jennifer H (2002) Breast imaging reporting and data system (BI-RADS). Radiol Clin North Am 40(3):409\u2013430","journal-title":"Radiol Clin North Am"},{"issue":"6","key":"2391_CR23","doi-asserted-by":"publisher","first-page":"891","DOI":"10.3390\/jcm8060891","volume":"8","author":"A Fanizzi","year":"2019","unstructured":"Fanizzi A, Losurdo L, Basile TMA, Bellotti R, Bottigli U, Delogu P, Diacono D, Didonna V, Fausto A, Lombardi A, Lorusso V, Massafra R, Tangaro S, Forgia DL (2019) Fully automated support system for diagnosis of breast cancer in contrast-enhanced spectral mammography images. J Clin Med 8(6):891","journal-title":"J Clin Med"},{"issue":"11","key":"2391_CR24","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.3390\/e21111110","volume":"21","author":"L Liliana","year":"2019","unstructured":"Liliana L, Annarita F, Basile Teresa Maria A, Roberto B, La Forgia D (2019) Radiomics analysis on contrast-enhanced spectral mammography images for breast cancer diagnosis: a pilot study. Entropy 21(11):1110","journal-title":"Entropy"},{"key":"2391_CR25","first-page":"09","volume":"10","author":"LF Daniele","year":"2020","unstructured":"Daniele LF, Annarita F, Francesco C, Roberto B, Vittorio D, Vito L, Marco M, Raffaella M, Pasquale T, Sabina T, Michele T, Maria P, Alfredo Z (2020) Radiomic analysis in contrast-enhanced spectral mammography for predicting breast cancer histological outcome. Diagnostics 10:09","journal-title":"Diagnostics"},{"key":"2391_CR26","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"2391_CR27","unstructured":"Kaiming H, Xiangyu Z, Shaoqing R, Jian S (2016). Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778"},{"key":"2391_CR28","unstructured":"Jie H, Li\u00a0S, Gang S (2018). Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141"},{"key":"2391_CR29","unstructured":"Saining X, Ross G, Piotr D, Zhuowen T, Kaiming H ( 2017). Aggregated residual transformations for deep neural networks. In: Proceedings - 30th IEEE conference on computer vision and pattern recognition, CVPR 2017, vol 2017, pp 5987\u20135995"},{"key":"2391_CR30","doi-asserted-by":"crossref","unstructured":"Zagoruyko S, Komodakis N (2016). Wide residual networks. In: British machine vision conference 2016, BMVC 2016, pp 87.1\u201387.12","DOI":"10.5244\/C.30.87"},{"key":"2391_CR31","doi-asserted-by":"crossref","unstructured":"Gao S, Cheng M-M, Zhao K, Zhang X-Y, Yang M-H, Torr PHS (2019). Res2net: a new multi-scale backbone architecture. IEEE transactions on pattern analysis and machine intelligence, pp :1\u20131, 08","DOI":"10.1109\/TPAMI.2019.2938758"},{"key":"2391_CR32","doi-asserted-by":"crossref","unstructured":"Peng Qi, Wei Zhou, Jizhong Han (2017) A method for stochastic L-BFGS optimization, vol 434, pp 156\u2013160","DOI":"10.1109\/ICCCBDA.2017.7951902"},{"key":"2391_CR33","doi-asserted-by":"crossref","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: Proceedings of the IEEE international conference on computer vision, pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02391-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-021-02391-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02391-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T06:54:01Z","timestamp":1622444041000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-021-02391-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,8]]},"references-count":33,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["2391"],"URL":"https:\/\/doi.org\/10.1007\/s11548-021-02391-4","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,8]]},"assertion":[{"value":"28 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}