{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T01:20:41Z","timestamp":1779153641438,"version":"3.51.4"},"reference-count":22,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1109\/cloudtech.2018.8713352","type":"proceedings-article","created":{"date-parts":[[2019,5,13]],"date-time":"2019-05-13T23:06:50Z","timestamp":1557788810000},"page":"1-6","source":"Crossref","is-referenced-by-count":35,"title":["Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning"],"prefix":"10.1109","author":[{"given":"Mohammed","family":"Benjelloun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed El","family":"Adoui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed Amine","family":"Larhmam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sidi Ahmed","family":"Mahmoudi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1002\/mp.12079","article-title":"Using deep learning to segment breast and fibroglandular tissue in mri volumes","volume":"44","author":"ufuk dalm1?","year":"2017","journal-title":"Medical Physics"},{"key":"ref11","first-page":"478","author":"moeskops","year":"2016","journal-title":"Deep learning for multitask medical image segmentation in multiple modalities"},{"key":"ref12","first-page":"565","author":"milletari","year":"2016","journal-title":"V-Net Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation"},{"key":"ref13","first-page":"415","author":"ferdinand christ","year":"2016","journal-title":"Automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation","volume":"36","author":"konstantinos","year":"2017","journal-title":"Medical Image Analysis"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.neuroimage.2018.03.065","article-title":"Bayesian convolutional neural network based mri brain extraction on nonhuman primates","volume":"175","author":"gengyan","year":"2018","journal-title":"NeuroImage"},{"key":"ref16","first-page":"56","author":"el adoui","year":"2017","journal-title":"Analyzing breast tumor heterogeneity to predict the response to chemotherapy using 3d mr images registration"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1117\/12.535112"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1002\/9781118711897.ch22"},{"key":"ref19","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-018-1790-y"},{"key":"ref3","first-page":"109","article-title":"Breast cancer heterogeneity analysis as index of response to treatment using mri images: A review","volume":"9","author":"el adoui","year":"2017","journal-title":"Imaging in Medicine"},{"key":"ref6","first-page":"3431","author":"long","year":"2015","journal-title":"Fully Convolutional Networks for Semantic Segmentation"},{"key":"ref5","first-page":"234","author":"ronneberger","year":"2015","journal-title":"U-net Convolutional networks for biomedical image segmentation"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1109\/TMI.2016.2528129","article-title":"Automatic detection of cerebral microbleeds from mr images via 3d convolutional neural networks","volume":"35","author":"qi","year":"2016","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/42.476112"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1002\/mrm.24782","article-title":"Dce-mri analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: Pilot study findings","volume":"71","author":"xia","year":"2014","journal-title":"Magnetic Resonance in Medicine"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-011-2108-2"},{"key":"ref9","author":"hwang","year":"2016","journal-title":"Self-transfer learning for fully weakly supervised object localization"},{"key":"ref20","author":"bouthillier","year":"2015","journal-title":"Dropout as data augmentation"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.4372"},{"key":"ref21","first-page":"195","author":"ahmed mahmoudi","year":"2017","journal-title":"Cloud-based platform for computer vision applications"}],"event":{"name":"2018 4th International Conference on Cloud Computing Technologies and Applications (Cloudtech)","location":"Brussels, Belgium","start":{"date-parts":[[2018,11,26]]},"end":{"date-parts":[[2018,11,28]]}},"container-title":["2018 4th International Conference on Cloud Computing Technologies and Applications (Cloudtech)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8698773\/8713330\/08713352.pdf?arnumber=8713352","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,3]],"date-time":"2019-06-03T23:41:44Z","timestamp":1559605304000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8713352\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1109\/cloudtech.2018.8713352","relation":{},"subject":[],"published":{"date-parts":[[2018,11]]}}}