{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T04:21:09Z","timestamp":1777954869094,"version":"3.51.4"},"reference-count":67,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:00:00Z","timestamp":1661472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2013Portugal","award":["UIDB\/00645\/2020"],"award-info":[{"award-number":["UIDB\/00645\/2020"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2013Portugal","award":["EXPL\/CCI-COM\/0656\/2021"],"award-info":[{"award-number":["EXPL\/CCI-COM\/0656\/2021"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2013Portugal","award":["UIDB\/00408\/2020"],"award-info":[{"award-number":["UIDB\/00408\/2020"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2013Portugal","award":["UIDP\/00408\/2020"],"award-info":[{"award-number":["UIDP\/00408\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Breast cancer was the most diagnosed cancer in 2020. Several thousand women continue to die from this disease. A better and earlier diagnosis may be of great importance to improving prognosis, and that is where Artificial Intelligence (AI) could play a major role. This paper surveys different applications of AI in Breast Imaging. First, traditional Machine Learning and Deep Learning methods that can detect the presence of a lesion and classify it into benign\/malignant\u2014which could be important to diminish reading time and improve accuracy\u2014are analyzed. Following that, researches in the field of breast cancer risk prediction using mammograms\u2014which may be able to allow screening programs customization both on periodicity and modality\u2014are reviewed. The subsequent section analyzes different applications of augmentation techniques that allow to surpass the lack of labeled data. Finally, still concerning the absence of big datasets with labeled data, the last section studies Self-Supervised learning, where AI models are able to learn a representation of the input by themselves. This review gives a general view of what AI can give in the field of Breast Imaging, discussing not only its potential but also the challenges that still have to be overcome.<\/jats:p>","DOI":"10.3390\/jimaging8090228","type":"journal-article","created":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T03:34:56Z","timestamp":1661744096000},"page":"228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["AI in Breast Cancer Imaging: A Survey of Different Applications"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5524-2846","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Mendes","sequence":"first","affiliation":[{"name":"Faculdade de Ci\u00eancias, Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]},{"given":"Jos\u00e9","family":"Domingues","sequence":"additional","affiliation":[{"name":"Faculdade de Ci\u00eancias, LASIGE, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]},{"given":"Helena","family":"Aidos","sequence":"additional","affiliation":[{"name":"Faculdade de Ci\u00eancias, LASIGE, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6371-3310","authenticated-orcid":false,"given":"Nuno","family":"Garcia","sequence":"additional","affiliation":[{"name":"Faculdade de Ci\u00eancias, LASIGE, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8048-7896","authenticated-orcid":false,"given":"Nuno","family":"Matela","sequence":"additional","affiliation":[{"name":"Faculdade de Ci\u00eancias, Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA-Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21708","article-title":"Cancer statistics, 2022","volume":"72","author":"Siegel","year":"2022","journal-title":"CA-Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"438","DOI":"10.3322\/caac.21583","article-title":"Breast cancer statistics, 2019","volume":"69","author":"DeSantis","year":"2019","journal-title":"CA-Cancer J. Clin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1097\/01.SLA.0000059969.64262.87","article-title":"Rating the Risk Factors for Breast Cancer","volume":"237","author":"Singletary","year":"2003","journal-title":"Ann. Surg."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1038\/303767a0","article-title":"\u2018Hormonal\u2019 risk factors, \u2018breast tissue age\u2019and the age-incidence of breast cancer","volume":"303","author":"Pike","year":"1983","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.bbcan.2015.06.002","article-title":"Established breast cancer risk factors and risk of intrinsic tumor subtypes","volume":"1856","author":"Barnard","year":"2015","journal-title":"Biochim. Biophys. Acta Rev. Cancer"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/bcr2942","article-title":"Mammographic density and breast cancer risk: Current understanding and future prospects","volume":"13","author":"Boyd","year":"2011","journal-title":"Breast Cancer Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1118\/1.598851","article-title":"Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: Feature selection","volume":"27","author":"Huo","year":"2000","journal-title":"Med. Phys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1093\/ije\/29.1.11","article-title":"Mammographic parenchymal patterns and risk of breast cancer at and after a prevalence screen in Singaporean women","volume":"29","author":"Jakes","year":"2000","journal-title":"Int. J. Epidemiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rcl.2017.06.004","article-title":"Screening for breast cancer","volume":"55","author":"Niell","year":"2017","journal-title":"Radiol. Clin."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.soncn.2017.02.009","article-title":"Early detection and screening for breast cancer","volume":"33","author":"Coleman","year":"2017","journal-title":"Semin. Oncol. Nurs."},{"key":"ref_12","first-page":"1","article-title":"Benefits and harms of mammography screening","volume":"17","author":"Lousdal","year":"2015","journal-title":"Breast Cancer Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1053\/j.sult.2017.09.006","article-title":"The role of ultrasound in breast cancer screening: The case for and against ultrasound","volume":"39","author":"Geisel","year":"2018","journal-title":"Semin. Ultrasound CT MR"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1016\/S0140-6736(11)61350-0","article-title":"MRI for breast cancer screening, diagnosis, and treatment","volume":"378","author":"Morrow","year":"2011","journal-title":"Lancet"},{"key":"ref_15","first-page":"274","article-title":"Breast tomosynthesis: State of the art","volume":"61","year":"2019","journal-title":"Radiolog\u00eda"},{"key":"ref_16","unstructured":"Ikeda, D., and Miyake, K. (2016). Breast Imaging: The Requisites, Elsevier."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fischer, U., and Baum, F. (2011). Interventional Breast Imaging: Ultrasound, Mammography, and MR Guidance Techniques, Thieme.","DOI":"10.1055\/b-0034-74234"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1016\/j.nima.2006.08.134","article-title":"Computer aided diagnosis based on medical image processing and artificial intelligence methods","volume":"569","author":"Stoitsis","year":"2006","journal-title":"Nucl. Instrum. Methods Phys. Res. A Accel. Spectrom. Detect. Assoc. Equip."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ongsulee, P. (2017, January 22\u201324). Artificial intelligence, machine learning and deep learning. Proceedings of the 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE), Bangkok, Thailand.","DOI":"10.1109\/ICTKE.2017.8259629"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","article-title":"Machine learning and deep learning","volume":"31","author":"Janiesch","year":"2021","journal-title":"Electron. Mark."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1148\/radiol.2018171820","article-title":"Current applications and future impact of machine learning in radiology","volume":"288","author":"Choy","year":"2018","journal-title":"Radiology"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tuceryan, M., and Jain, A.K. (1993). Texture analysis. Handbook of Pattern Recognition and Computer Vision, World Scientific Publishing Co., Inc.","DOI":"10.1142\/9789814343138_0010"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.2214\/ajr.126.6.1130","article-title":"Breast patterns as an index of risk for developing breast cancer","volume":"126","author":"Wolfe","year":"1976","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1158\/1055-9965.EPI-08-0631","article-title":"Texture features from mammographic images and risk of breast cancer","volume":"18","author":"Manduca","year":"2009","journal-title":"Cancer Epidemiol. Biomark. Prev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1148\/radiol.2019181113","article-title":"Digital mammography in breast cancer: Additive value of radiomics of breast parenchyma","volume":"291","author":"Li","year":"2019","journal-title":"Radiology"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_27","first-page":"151","article-title":"Grey Level Co-Occurrence Matrices: Generalisation and Some New Features","volume":"2","author":"Vadakkenveettil","year":"2012","journal-title":"Int. J. Comput. Sci. Eng. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/S0146-664X(75)80008-6","article-title":"Texture analysis using gray level run lengths","volume":"4","author":"Galloway","year":"1975","journal-title":"Comput. Graph. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1118\/1.1644514","article-title":"Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: Effect of ROI size and location","volume":"31","author":"Li","year":"2004","journal-title":"Med. Phys."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2100073","DOI":"10.1002\/aisy.202100073","article-title":"Machine-Learning-Assisted Intelligent Imaging Flow Cytometry: A Review","volume":"3","author":"Luo","year":"2021","journal-title":"Adv. Intell. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1016\/j.acra.2009.07.024","article-title":"Computer-aided diagnosis of soft-tissue tumors using sonographic morphologic and texture features","volume":"16","author":"Chen","year":"2009","journal-title":"Acad. Radiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"275","DOI":"10.2147\/MDER.S206973","article-title":"An automated mammogram classification system using modified support vector machine","volume":"12","author":"Kayode","year":"2019","journal-title":"Med. Devices"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1007\/s00521-012-1025-z","article-title":"Texture-based features for classification of mammograms using decision tree","volume":"23","author":"Mohanty","year":"2013","journal-title":"Neural. Comput. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wei, M., Du, Y., Wu, X., and Zhu, J. (2019, January 25\u201327). Automatic classification of benign and malignant breast tumors in ultrasound image with texture and morphological features. Proceedings of the 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID), Xiamen, China.","DOI":"10.1109\/ICASID.2019.8925194"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1016\/j.acra.2008.06.005","article-title":"Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI","volume":"15","author":"Nie","year":"2008","journal-title":"Acad. Radiol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mendes, J., and Matela, N. (2021). Breast cancer risk assessment: A review on mammography-based approaches. J. Imaging, 7.","DOI":"10.3390\/jimaging7060098"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.1016\/j.acra.2013.08.020","article-title":"Prediction of near-term breast cancer risk based on bilateral mammographic feature asymmetry","volume":"20","author":"Tan","year":"2013","journal-title":"Acad. Radiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4149","DOI":"10.1118\/1.4921996","article-title":"Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment","volume":"42","author":"Zheng","year":"2015","journal-title":"Med. Phys."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"035016","DOI":"10.1088\/1361-6560\/aafabd","article-title":"A novel method of determining breast cancer risk using parenchymal textural analysis of mammography images on an Asian cohort","volume":"64","author":"Tan","year":"2019","journal-title":"Phys. Med. Biol."},{"key":"ref_41","first-page":"517","article-title":"An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology","volume":"Volume 9785","author":"Qiu","year":"2016","journal-title":"Medical Imaging 2016: Computer-Aided Diagnosis"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1148\/radiol.2019182716","article-title":"A deep learning mammography-based model for improved breast cancer risk prediction","volume":"292","author":"Yala","year":"2019","journal-title":"Radiology"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1109\/JAS.2017.7510583","article-title":"Generative adversarial networks: Introduction and outlook","volume":"4","author":"Wang","year":"2017","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_44","unstructured":"Goodfellow, I.J. (2017). NIPS 2016 Tutorial: Generative Adversarial Networks. arXiv."},{"key":"ref_45","unstructured":"Osuala, R., Kushibar, K., Garrucho, L., Linardos, A., Szafranowska, Z., Klein, S., Glocker, B., Diaz, O., and Lekadir, K. (2021). A review of generative adversarial networks in cancer imaging: New applications, new solutions. arXiv."},{"key":"ref_46","unstructured":"Korkinof, D., Rijken, T., O\u2019Neill, M., Yearsley, J., Harvey, H., and Glocker, B. (2018). High-resolution mammogram synthesis using progressive generative adversarial networks. arXiv."},{"key":"ref_47","unstructured":"Korkinof, D., Heindl, A., Rijken, T., Harvey, H., and Glocker, B. (2019, January 8\u201310). MammoGAN: High-resolution synthesis of realistic mammograms. Proceedings of the International Conference on Medical Imaging with Deep Learning\u2013Extended Abstract Track, London, UK."},{"key":"ref_48","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017, January 4\u20139). GANs trained by a two time-scale update rule converge to a local nash equilibrium. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"155362","DOI":"10.1109\/ACCESS.2020.3019327","article-title":"Classification of breast cancer histopathological images using discriminative patches screened by generative adversarial networks","volume":"8","author":"Man","year":"2020","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"105006","DOI":"10.1088\/1361-6560\/ab7e7f","article-title":"Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks","volume":"65","author":"Ma","year":"2020","journal-title":"Phys. Med. Biol."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wu, E., Wu, K., Cox, D., and Lotter, W. (2018). Conditional infilling GANs for data augmentation in mammogram classification. Image Analysis for Moving Organ, Breast, and Thoracic Images, Springer.","DOI":"10.1007\/978-3-030-00946-5_11"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Senaras, C., Niazi, M.K.K., Sahiner, B., Pennell, M.P., Tozbikian, G., Lozanski, G., and Gurcan, M.N. (2018). Optimized generation of high-resolution phantom images using cGAN: Application to quantification of Ki67 breast cancer images. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0196846"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Fujioka, T., Mori, M., Kubota, K., Kikuchi, Y., Katsuta, L., Adachi, M., Oda, G., Nakagawa, T., Kitazume, Y., and Tateishi, U. (2019). Breast ultrasound image synthesis using deep convolutional generative adversarial networks. Diagnostics, 9.","DOI":"10.3390\/diagnostics9040176"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-022-09929-9","article-title":"A generative adversarial network for synthetization of regions of interest based on digital mammograms","volume":"12","author":"Oyelade","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"6399","DOI":"10.1007\/s13369-020-04480-z","article-title":"RDA-UNET-WGAN: An accurate breast ultrasound lesion segmentation using wasserstein generative adversarial networks","volume":"45","author":"Negi","year":"2020","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_58","unstructured":"Li, Z., Cui, Z., Wang, S., Qi, Y., Ouyang, X., Chen, Q., Yang, Y., Xue, Z., Shen, D., and Cheng, J.Z. (October, January 27). Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_60","unstructured":"Gao, Y., Wang, X., Zhang, T., Han, L., Beets-Tan, R., and Mann, R. (2022, January 6\u20138). Self-supervised learning of mammograms with pathology aware. Proceedings of the Medical Imaging with Deep Learning, Zurich, Switzerland."},{"key":"ref_61","unstructured":"Miller, J.D., Arasu, V.A., Pu, A.X., Margolies, L.R., Sieh, W., and Shen, L. (2022). Self-Supervised Deep Learning to Enhance Breast Cancer Detection on Screening Mammography. arXiv."},{"key":"ref_62","unstructured":"Ouyang, X., Che, J., Chen, Q., Li, Z., Zhan, Y., Xue, Z., Wang, Q., Cheng, J.Z., and Shen, D. (27\u20131, January 27). Self-adversarial learning for detection of clustered microcalcifications in mammograms. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Srinidhi, C.L., and Martel, A.L. (2021, January 10\u201317). Improving Self-supervised Learning with Hardness-aware Dynamic Curriculum Learning: An Application to Digital Pathology. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00069"},{"key":"ref_64","unstructured":"Truong, T., Mohammadi, S., and Lenga, M. (2021, January 6\u20137). How Transferable Are Self-supervised Features in Medical Image Classification Tasks?. Proceedings of the Machine Learning for Health, PMLR, Virtual Event."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"20190580","DOI":"10.1259\/bjr.20190580","article-title":"CAD and AI for breast cancer\u2014Recent development and challenges","volume":"93","author":"Chan","year":"2019","journal-title":"Br. J. Radiol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"20043","DOI":"10.1007\/s11042-022-12332-1","article-title":"Mammogram breast cancer CAD systems for mass detection and classification: A review","volume":"81","author":"Hassan","year":"2022","journal-title":"Multimed. Tools. Appl."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.crad.2019.02.006","article-title":"Artificial intelligence in breast imaging","volume":"74","author":"Le","year":"2019","journal-title":"Clin. Radiol."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/9\/228\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:15:39Z","timestamp":1760141739000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/9\/228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,26]]},"references-count":67,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["jimaging8090228"],"URL":"https:\/\/doi.org\/10.3390\/jimaging8090228","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,26]]}}}