{"authors":[{"id":null,"fullName":"Garrucho, Lidia","name":"Lidia","surname":"Garrucho","rank":1,"pid":{"id":{"scheme":"orcid_pending","value":"0000-0002-3105-2773"},"provenance":null}},{"id":null,"fullName":"Osuala, Richard","name":"Richard","surname":"Osuala","rank":2,"pid":{"id":{"scheme":"orcid_pending","value":"0000-0003-1835-8564"},"provenance":null}}],"openAccessColor":null,"publiclyFunded":null,"eoscIfGuidelines":null,"type":"software","language":{"code":"und","label":"Undetermined"},"countries":null,"subjects":[{"subject":{"scheme":"FOS","value":"0202 electrical engineering, electronic engineering, information engineering"},"provenance":null},{"subject":{"scheme":"FOS","value":"02 engineering and technology"},"provenance":null},{"subject":{"scheme":"keyword","value":"Generative Adversarial Networks, Breast Density, CYCLEGAN, Mammography, Synthetic Data, Domain-Adaptation"},"provenance":null}],"mainTitle":"CYCLEGAN Model for Mammogram Low-to-High Breast Density Translation ONLY CC (Trained on CSAW)","subTitle":null,"descriptions":["<strong>Trained On:</strong> Trained on CSAW dataset, and only on craniocaudal oblique (CC) view images. <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 </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() # Translate 10 images from low-to-high breast density generators.generate(model_id=\"00016_CYCLEGAN_MMG_DENSITY_CSAW_CC\",num_samples=10) # Translate 10 images from high-to-low breast density generators.generate(model_id=\"00016_CYCLEGAN_MMG_DENSITY_CSAW_CC\",num_samples=10, low_to_high=False)</code></pre> <strong>Description:</strong> A cycle generative adversarial network (CycleGAN) that generates mammograms with high breast density from an original mammogram e.g. with low-breast density. The CycleGAN was trained using normal (without pathologies) digital mammograms from CSAW dataset (Dembrower et al., 2020). <strong>Only CC</strong> view images were used for training. The uploaded ZIP file contains the files <em>CycleGAN_high_density.pth</em> (model weights), <em>__init__.py</em> (image generation method and utils), a <em>license,</em> and the GAN model architecture (in pytorch) below the /src folder. Note: The images in the <em>CycleGAN_high_density/images </em>are synthetically generated low breast density mammograms. These images were generated with a <strong>high-to-low</strong> breast density mammogram translating cycleGAN (trained on BCDR) with the same architecture as the present one. These synthetic mammogram images were uploaded instead of original images to avoid any conflicts in regard to copyright and intellectual property. The synthetic images are example images that <em>medigan</em> users may translate to examine how the present cycleGAN works. In particular, these synthetic images will be randomly translated if the <em>medigan</em> users do not provide their own input images to the present cycleGAN.","{\"references\": [\"Osuala, Richard et al. (2021). 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