{"authors":[{"id":null,"fullName":"Richard Osuala","name":null,"surname":"Richard Osuala","rank":1,"pid":{"id":{"scheme":"orcid_pending","value":"0000-0003-1835-8564"},"provenance":null}},{"id":null,"fullName":"Zuzanna Szafranowska","name":null,"surname":"Zuzanna Szafranowska","rank":2,"pid":{"id":{"scheme":"orcid_pending","value":"0000-0003-4030-9726"},"provenance":null}},{"id":null,"fullName":"Bennet Breier","name":null,"surname":"Bennet Breier","rank":3,"pid":null}],"openAccessColor":null,"publiclyFunded":null,"eoscIfGuidelines":null,"type":"software","language":{"code":"und","label":"Undetermined"},"countries":null,"subjects":[{"subject":{"scheme":"keyword","value":"Generative Adversarial Networks, Synthetic Data, DCGAN, Mammography, IWBI2022"},"provenance":null},{"subject":{"scheme":"FOS","value":"0202 electrical engineering, electronic engineering, information engineering"},"provenance":null},{"subject":{"scheme":"FOS","value":"02 engineering and technology"},"provenance":null}],"mainTitle":"DCGAN Model for Mammogram MASS Patch Generation (Trained on BCDR)","subTitle":null,"descriptions":["<strong>Usage:</strong> This GAN is used as part of the <strong><em>medigan</em></strong> library. This GANs metadata is therefore stored in and retrieved from <em>medigan-model's</em> config file. <em>medigan </em>is an open-source Python library on Github that allows developers and researchers to easily add synthetic imaging data into their model training pipelines. <em>medigan</em> is documented here and can be used via pip install: <pre><code class=\"language-python\">pip install medigan</code></pre> To run this model in medigan, use the following commands. <pre><code class=\"language-python\"># import medigan and initialize generators from medigan import Generators generators = Generators() # Generate 10 images generators.generate(model_id=\"00005_DCGAN_MMG_MASS_ROI\",num_samples=10)</code></pre> <strong>Description:</strong> A deep convolutional generative adversarial network (DCGAN) that generates mass patches of mammograms. Pixel dimensions are 128x128. The DCGAN was trained on MMG patches from the BCDR dataset (Lopez et al, 2012). 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A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions 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