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Azamjah N, Soltan-Zadeh Y, Zayeri F. Global trend of breast cancer mortality rate: A 25-year study. Asian Pac J Cancer Prev 2019; 20:2015\u20132020.","DOI":"10.31557\/APJCP.2019.20.7.2015"},{"key":"2","doi-asserted-by":"crossref","unstructured":"2. Huang J, Chan PS, Lok V, et al. Global incidence and mortality of breast cancer: A trend analysis. Aging (Albany NY) 2021; 13:5748\u20135803.","DOI":"10.18632\/aging.202502"},{"key":"3","doi-asserted-by":"crossref","unstructured":"3. Arnold M, Morgan E, Rumgay H, et al. Current and future burden of breast cancer: Global statistics for 2020 and 2040. Breast 2022; 66:15\u201323.","DOI":"10.1016\/j.breast.2022.08.010"},{"key":"4","doi-asserted-by":"crossref","unstructured":"4. Lei S, Zheng R, Zhang S, et al. Global patterns of breast cancer incidence and mortality: A population-based cancer registry data analysis from 2000 to 2020. 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Diffusion-weighted MRI for unenhanced breast cancer screening. Radiology 2019; 293:504\u2013520.","DOI":"10.1148\/radiol.2019182789"},{"key":"9","doi-asserted-by":"crossref","unstructured":"9. Comstock CE, Gatsonis C, Newstead GM, et al. Comparison of abbreviated breast MRI vs digital breast tomosynthesis for breast cancer detection among women with dense breasts undergoing screening. JAMA 2020; 323:746\u2013756.","DOI":"10.1001\/jama.2020.0572"},{"key":"10","doi-asserted-by":"crossref","unstructured":"10. Vandermeer FQ, Bluemke DA. Breast MRI: State of the art. Cancer Invest 2007; 25:384\u2013392.","DOI":"10.1080\/07357900701360013"},{"key":"11","doi-asserted-by":"crossref","unstructured":"11. Xiao Z, Su Y, Deng Z, Zhang W. Efficient combination of CNN and transformer for dual-teacher uncertainty-guided semi-supervised medical image segmentation. Comput Methods Programs Biomed 2022; 226:107099.","DOI":"10.1016\/j.cmpb.2022.107099"},{"key":"12","doi-asserted-by":"crossref","unstructured":"12. Khan RF, Lee BD, Lee MS. Transformers in medical image segmentation: A narrative review. Quant Imaging Med Surg 2023; 13:8747\u20138767.","DOI":"10.21037\/qims-23-542"},{"key":"13","doi-asserted-by":"crossref","unstructured":"13. Shamshad F, Khan S, Zamir SW, et al. Transformers in medical imaging: A survey. Med Image Anal 2023; 88:102802.","DOI":"10.1016\/j.media.2023.102802"},{"key":"14","doi-asserted-by":"crossref","unstructured":"14. Karimi D, Dou H, Gholipour A. Medical image segmentation using transformer networks. IEEE Access 2022; 10:29322\u201329332.","DOI":"10.1109\/ACCESS.2022.3156894"},{"key":"15","doi-asserted-by":"crossref","unstructured":"15. Yin XX, Jian Y, Shen J, Wu J, Zhang Y, Wang W. Focal boundary dice: Improved breast tumor segmentation from MRI Scan. J Cancer 2023; 14:717\u2013736.","DOI":"10.7150\/jca.82592"},{"key":"16","unstructured":"16. Hu M, Li Y, Yang X. BreastSAM: A study of segment anything model for breast tumor detection in ultrasound images. arXiv:2305.12447 [eess.IV]."},{"key":"17","doi-asserted-by":"crossref","unstructured":"17. Xia Y, Liu W, Yang D, Wang H, Wang H, Jiang M. Early stage tumor segmentation in breast MRI using shape enhanced U-Net. Biomed Signal Process Control 2024; 93:106198.","DOI":"10.1016\/j.bspc.2024.106198"},{"key":"18","doi-asserted-by":"crossref","unstructured":"18. Rehman NU, Wang J, Weiyan H, et al. Edge of discovery: Enhancing breast tumor MRI analysis with boundary-driven deep learning. Biomed Signal Process Control 2024; 95:106291.","DOI":"10.1016\/j.bspc.2024.106291"},{"key":"19","doi-asserted-by":"crossref","unstructured":"19. Zhang J, Wu J, Zhou XS, Shi F, Shen D. 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Radiologist-level performance by using deep learning for segmentation of breast cancers on MRI scans. Radiol Artif Intell 2021; 4:e200231.","DOI":"10.1148\/ryai.200231"},{"key":"23","doi-asserted-by":"crossref","unstructured":"23. Madani M, Behzadi MM, Nabavi S. The role of deep learning in advancing breast cancer detection using different imaging modalities: A systematic review. Cancers (Basel) 2022; 14:5334.","DOI":"10.3390\/cancers14215334"},{"key":"24","doi-asserted-by":"crossref","unstructured":"24. Bilic A, Chen C. BC-MRI-SEG. A breast cancer MRI tumor segmentation benchmark. Proceedings of the 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), Orlando, 2024; 674-678.","DOI":"10.1109\/ICHI61247.2024.00107"},{"key":"25","doi-asserted-by":"crossref","unstructured":"25. Chitalia R, Pati S, Bhalerao M, et al. Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657\/I-SPY1. Sci Data 2022; 9:440.","DOI":"10.1038\/s41597-022-01555-4"},{"key":"26","doi-asserted-by":"crossref","unstructured":"26. Zhao X, Liao Y, Xie J, et al. BreastDM: A DCE-MRI dataset for breast tumor image segmentation and classification. Comput Biol Med 2023; 164:107255.","DOI":"10.1016\/j.compbiomed.2023.107255"},{"key":"27","unstructured":"27. Meyer CR, Chenevert TL, Galb\u00e1n CJ, et al. RIDER Breast MRI. The Cancer Imaging Archive 2015. (Accessed: May 1, 2026)"},{"key":"28","doi-asserted-by":"crossref","unstructured":"28. Saha A, Harowicz MR, Grimm LJ, et al. A machine learning approach to radiogenomics of breast cancer: A study of 922 subjects and 529 DCE-MRI features. Br J Cancer 2018; 119:508\u2013516.","DOI":"10.1038\/s41416-018-0185-8"},{"key":"29","doi-asserted-by":"crossref","unstructured":"29. Rukundo O. Effects of image size on deep learning. Electronics (Basel) 2023; 12:985.","DOI":"10.3390\/electronics12040985"},{"key":"30","doi-asserted-by":"crossref","unstructured":"30. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016; pp. 770-778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"31","doi-asserted-by":"crossref","unstructured":"31. Papanastasiou G, Dikaios N, Huang J, Wang C, Yang G. Is attention all you need in medical image analysis? A Review. IEEE J Biomed Health Inform 2024; 28:1398\u20131411.","DOI":"10.1109\/JBHI.2023.3348436"},{"key":"32","unstructured":"32. Ioffe S, Szegedy C. Batch Normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167 [cs.LG]."},{"key":"33","doi-asserted-by":"crossref","unstructured":"33. Ginsburg O, Yip CH, Brooks A, et al. Breast cancer early detection: A phased approach to implementation. Cancer 2020; 126 Suppl 10 (Suppl 10):2379-2393.","DOI":"10.1002\/cncr.32887"},{"key":"34","doi-asserted-by":"crossref","unstructured":"34. Wilkinson L, Gathani T. Understanding breast cancer as a global health concern. Br J Radiol 2022; 95:20211033.","DOI":"10.1259\/bjr.20211033"},{"key":"35","doi-asserted-by":"crossref","unstructured":"35. Pashayan N, Antoniou AC, Ivanus U, et al. Personalized early detection and prevention of breast cancer: ENVISION consensus statement. Nat Rev Clin Oncol 2020; 17:687\u2013705.","DOI":"10.1038\/s41571-020-0388-9"},{"key":"36","doi-asserted-by":"crossref","unstructured":"36. Sch\u00fcnemann HJ, Lerda D, Quinn C, et al. Breast cancer screening and diagnosis: A synopsis of the European Breast Guidelines. Ann Intern Med 2020; 172:46\u201356.","DOI":"10.7326\/M19-2125"},{"key":"37","doi-asserted-by":"crossref","unstructured":"37. Ghorbian M, Ghorbian S. Usefulness of machine learning and deep learning approaches in screening and early detection of breast cancer. Heliyon 2023; 9:e22427.","DOI":"10.1016\/j.heliyon.2023.e22427"},{"key":"38","doi-asserted-by":"crossref","unstructured":"38. 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