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The performance evaluation of each examined network is addressed in two training scenarios: the first involves initializing the network with pre-trained weights, while for the second the networks are initialized in a random fashion. Extensive experimental results show the superior performance achieved in the case of fine-tuning a pretrained network compared to training from scratch.<\/jats:p>","DOI":"10.3390\/jimaging5030037","type":"journal-article","created":{"date-parts":[[2019,3,14]],"date-time":"2019-03-14T04:15:29Z","timestamp":1552536929000},"page":"37","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":153,"title":["Deep Learning for Breast Cancer Diagnosis from Mammograms\u2014A Comparative Study"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4634-7419","authenticated-orcid":false,"given":"Lazaros","family":"Tsochatzidis","sequence":"first","affiliation":[{"name":"Visual Computing Group, Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece"}]},{"given":"Lena","family":"Costaridou","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, School of Medicine, University of Patras, 26504 Patras, Greece"}]},{"given":"Ioannis","family":"Pratikakis","sequence":"additional","affiliation":[{"name":"Visual Computing Group, Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.3322\/caac.21254","article-title":"Cancer statistics, 2015","volume":"65","author":"Siegel","year":"2015","journal-title":"CA Cancer J. 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