{"authors":[{"id":null,"fullName":"Joshi, Smriti","name":"Smriti","surname":"Joshi","rank":1,"pid":null},{"id":null,"fullName":"Osuala, Richard","name":"Richard","surname":"Osuala","rank":2,"pid":null}],"openAccessColor":null,"publiclyFunded":null,"eoscIfGuidelines":null,"type":"software","language":{"code":"und","label":"Undetermined"},"countries":null,"subjects":[{"subject":{"scheme":"keyword","value":"medigan, cycleGAN, GANs"},"provenance":null}],"mainTitle":"MEDIGAN MODEL UPLOAD: 00021_CYCLEGAN_Brain_MRI_T1_T2","subTitle":null,"descriptions":["<strong>Model ID:</strong> 00021_CYCLEGAN_BRAIN_MRI_T1_T2. <strong>Uploaded via:</strong> API <strong>Tags:</strong> ['Domain Adaptation', 'Brain MRI', 'Vestibular Schwanomma', 'Segmentation'] <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'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\"> from medigan import Generators </code></pre> <pre> <code class=\"language-python\"> generators = Generators() </code></pre> <pre> <code class=\"language-python\"> generators.generate(model_id=21,num_samples=10)</code></pre> <strong>Description from model config:</strong> : {\"title\": \"CycleGAN Brain MRI T1-T2 translation (trained on CrossMoDA 2021 dataset)\", \"provided_date\": \"2022\", \"trained_date\": \"2021\", \"provided_after_epoch\": 65, \"version\": \"1\", \"publication\": \"workshop 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