{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:29:51Z","timestamp":1742923791856,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811638794"},{"type":"electronic","value":"9789811638800"}],"license":[{"start":{"date-parts":[[2021,8,15]],"date-time":"2021-08-15T00:00:00Z","timestamp":1628985600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,15]],"date-time":"2021-08-15T00:00:00Z","timestamp":1628985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-16-3880-0_24","type":"book-chapter","created":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T11:02:58Z","timestamp":1628938978000},"page":"228-237","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Data Augmentation for Breast Cancer Mass Segmentation"],"prefix":"10.1007","author":[{"given":"Luc","family":"Caselles","sequence":"first","affiliation":[]},{"given":"Cl\u00e9ment","family":"Jailin","sequence":"additional","affiliation":[]},{"given":"Serge","family":"Muller","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,15]]},"reference":[{"issue":"2","key":"24_CR1","first-page":"87","volume":"65","author":"LA Torre","year":"2015","unstructured":"Torre, L.A., Bray, F., Siegel, R.L., Ferlay, J., Lortet-Tieulent, J., Jemal, A.: Global cancer statistics, 2012. CA: Cancer J. Clin. 65(2), 87\u2013108 (2015)","journal-title":"CA: Cancer J. Clin."},{"key":"24_CR2","volume-title":"Deep Learning for Medical Image Analysis","author":"SK Zhou","year":"2017","unstructured":"Zhou, S.K., Greenspan, H., Shen, D.: Deep Learning for Medical Image Analysis. Academic Press, Cambridge (2017)"},{"issue":"1","key":"24_CR3","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1002\/mp.13264","volume":"46","author":"B Sahiner","year":"2019","unstructured":"Sahiner, B., et al.: Deep learning in medical imaging and radiation therapy. Med. Phys. 46(1), e1\u2013e36 (2019)","journal-title":"Med. Phys."},{"issue":"1","key":"24_CR4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"L Shen","year":"2019","unstructured":"Shen, L., Margolies, L.R., Rothstein, J.H., Fluder, E., McBride, R., Sieh, W.: Deep learning to improve breast cancer detection on screening mammography. Sci. Rep. 9(1), 1\u201312 (2019)","journal-title":"Sci. Rep."},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Benzebouchi, N.E., Azizi, N., Ayadi, K.: A computer-aided diagnosis system for breast cancer using deep convolutional neural networks. In: Computational Intelligence in Data Mining, pp. 583\u2013593. Springer (2019)","DOI":"10.1007\/978-981-10-8055-5_52"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Taylor, L., Nitschke, G.: Improving deep learning using generic data augmentation. IEEE (2017)","DOI":"10.1109\/SSCI.2018.8628742"},{"issue":"1","key":"24_CR7","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 60 (2019)","journal-title":"J. Big Data"},{"key":"24_CR8","unstructured":"Hussain, Z., Gimenez, F., Yi, D., Rubin, D.: Differential data augmentation techniques for medical imaging classification tasks. In: 2017 AMIA Annual Symposium Proceedings, vol. 2017, p. 979. American Medical Informatics Association (2017)"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Castro, E., Cardoso, J.S., Pereira, J.: Elastic deformations for data augmentation in breast cancer mass detection. In: IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (2018)","DOI":"10.1109\/BHI.2018.8333411"},{"key":"24_CR10","unstructured":"Hauberg, S., Freifeld, O., Larsen, A.B.L., Fisher, J., Hansen, L.: Dreaming more data: class-dependent distributions over diffeomorphisms for learned data augmentation. In: Artificial Intelligence and Statistics, pp. 342\u2013350 (2016)"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Acero, J., et al.: SMOD - data augmentation based on statistical models of deformation to enhance segmentation in 2D cine cardiac MRI, pp. 361\u2013369, May 2019","DOI":"10.1007\/978-3-030-21949-9_39"},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Shen, Z., Xu, Z., Olut, S., Niethammer, M.: Anatomical data augmentation via fluid-based image registration. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 318\u2013328. Springer (2020)","DOI":"10.1007\/978-3-030-59716-0_31"},{"key":"24_CR13","volume-title":"Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts","author":"MA Sutton","year":"2009","unstructured":"Sutton, M.A., Orteu, J.J., Schreier, H.: Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts. Theory and Applications. Springer, Heidelberg (2009)"},{"issue":"6","key":"24_CR14","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1007\/s11340-006-9824-8","volume":"46","author":"G Besnard","year":"2006","unstructured":"Besnard, G., Hild, F., Roux, S.: \u201cFinite-element\u2019\u2019 displacement fields analysis from digital images: application to portevin-le ch\u00e2telier bands. Exp. Mech. 46(6), 789\u2013803 (2006)","journal-title":"Exp. Mech."},{"issue":"3","key":"24_CR15","doi-asserted-by":"publisher","first-page":"031902","DOI":"10.1118\/1.4789579","volume":"40","author":"SSJ Feng","year":"2013","unstructured":"Feng, S.S.J., Patel, B., Sechopoulos, I.: Objective models of compressed breast shapes undergoing mammography. Medical Physics 40(3), 031902 (2013)","journal-title":"Medical Physics"},{"issue":"10","key":"24_CR16","doi-asserted-by":"publisher","first-page":"2419","DOI":"10.1118\/1.1510128","volume":"29","author":"M Skarpathiotakis","year":"2002","unstructured":"Skarpathiotakis, M., et al.: Development of contrast digital mammography. Med. Phys. 29(10), 2419\u20132426 (2002)","journal-title":"Med. Phys."},{"issue":"3","key":"24_CR17","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1007\/s00330-010-1944-y","volume":"21","author":"C Dromain","year":"2011","unstructured":"Dromain, C., et al.: Dual-energy contrast-enhanced digital mammography: initial clinical results. Eur. Radiol. 21(3), 565\u2013574 (2011)","journal-title":"Eur. Radiol."},{"issue":"8","key":"24_CR18","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1016\/j.crad.2018.05.005","volume":"73","author":"JJ James","year":"2018","unstructured":"James, J.J., Tennant, S.L.: Contrast-enhanced spectral mammography (CESM). Clin. Radiol. 73(8), 715\u2013723 (2018)","journal-title":"Clin. Radiol."},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Gurummunirathnam, V., Yarlapati, N., Little, S., O\u2019Connor, N.E.: A deep residual architecture for skin lesion segmentation. In: OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis, pp. 277\u2013284. Springer (2018)","DOI":"10.1007\/978-3-030-01201-4_30"},{"issue":"8","key":"24_CR21","doi-asserted-by":"publisher","first-page":"e0221535","DOI":"10.1371\/journal.pone.0221535","volume":"14","author":"Z Zhuang","year":"2019","unstructured":"Zhuang, Z., Li, N., Joseph Raj, A.N., Mahesh, V.G.V., Qiu, S.: An RDAU-NET model for lesion segmentation in breast ultrasound images. PloS One 14(8), e0221535 (2019)","journal-title":"PloS One"},{"issue":"11","key":"24_CR22","doi-asserted-by":"publisher","first-page":"1826","DOI":"10.3390\/jcm8111826","volume":"8","author":"C-H Weng","year":"2019","unstructured":"Weng, C.-H., et al.: Artificial intelligence for automatic measurement of sagittal vertical axis using ResUnet framework. J. Clin. Med. 8(11), 1826 (2019)","journal-title":"J. Clin. Med."}],"container-title":["Lecture Notes in Electrical Engineering","Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-3880-0_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T11:08:11Z","timestamp":1673089691000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-3880-0_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,15]]},"ISBN":["9789811638794","9789811638800"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-3880-0_24","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"type":"print","value":"1876-1100"},{"type":"electronic","value":"1876-1119"}],"subject":[],"published":{"date-parts":[[2021,8,15]]},"assertion":[{"value":"15 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This research study was conducted retrospectively using anonymized human subject data made available by research partners (Dr Philippe Benillouche, CSE-Paris, France; Dr Weijun Peng, Shanghai Cancer Center, Fudan University, China; Dr Guixiang Zhang, Shanghai First People\u2019s Hospital, Medical College, Shanghai Jiaotong University, China). Applicable law and standards of ethic have been respected.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"MICAD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Imaging and Computer-Aided Diagnosis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Birmingham","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 March 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 March 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micad2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}