{"authors":[{"id":null,"fullName":"Osuala, Richard","name":"Richard","surname":"Osuala","rank":1,"pid":{"id":{"scheme":"orcid_pending","value":"0000-0003-1835-8564"},"provenance":null}},{"id":null,"fullName":"Szafranowska, Zuzanna","name":"Zuzanna","surname":"Szafranowska","rank":2,"pid":{"id":{"scheme":"orcid_pending","value":"0000-0003-4030-9726"},"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, Synthetic Data, DCGAN, Mammography"},"provenance":null}],"mainTitle":"DCGAN Model for Mammogram Calcification Region of Interest Generation (Trained on INbreast)","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=\"00001_DCGAN_MMG_CALC_ROI\",num_samples=10)</code></pre> <strong>Description:</strong> A deep convolutional generative adversarial network (DCGAN) that generates regions of interest (ROI) of mammograms containing benign and/or malignant calcifications. Pixel dimensions are 128x128. The DCGAN was trained on ROIs from the INbreast dataset (Moreira et al, 2012). The uploaded ZIP file contains the files <em>dcgan.pt</em> (model weights), <em>__init__.py </em>(image generation method and utils), a <em>README.md</em>, and the GAN model architecture (in pytorch) below the /src folder. <strong>Metadata:</strong> The source of the metadata displayed below is the global.json file in the medigan-models Github repository. <pre><code class=\"language-json\">{ \"00001_DCGAN_MMG_CALC_ROI\": { \"execution\": { \"package_name\": \"DCGAN\", \"package_link\": \"https://zenodo.org/record/5526998/files/DCGAN.zip?download=1\", \"model_name\": \"DCGAN\", \"extension\": \".pt\", \"image_size\": [ 128, 128 ], \"dependencies\": [ \"numpy\", \"Path\", \"torch\", \"opencv-contrib-python-headless\" ], \"generate_method\": { \"name\": \"generate\", \"args\": { \"base\": [ \"model_file\", \"num_samples\", \"output_path\", \"save_images\" ], \"custom\": { \"image_size\": 128 } } } }, \"selection\": { \"performance\": { \"SSIM\": null, \"MSE\": null, \"NSME\": null, \"PSNR\": null, \"IS\": null, \"FID\": null, \"turing_test\": null, \"downstream_task\": { \"CLF\": { \"trained_on_fake\": { \"accuracy\": null, \"precision\": null, \"recall\": null, \"f1\": null, \"specificity\": null, \"AUROC\": null, \"AUPRC\": null }, \"trained_on_real_and_fake\": {}, \"trained_on_real\": {} }, \"SEG\": { \"trained_on_fake\": { \"dice\": null, \"jaccard\": null, \"accuracy\": null, \"precision\": null, \"recall\": null, \"f1\": null }, \"trained_on_real_and_fake\": {}, \"trained_on_real\": {} } } }, \"use_cases\": [ \"classification\" ], \"organ\": [ \"breast\", \"breasts\", \"chest\" ], \"modality\": [ \"MMG\", \"Mammography\", \"Mammogram\", \"full-field digital\", \"full-field digital MMG\", \"full-field MMG\", \"full-field Mammography\", \"digital Mammography\", \"digital MMG\", \"x-ray mammography\" ], \"vendors\": [], \"centres\": [], \"function\": [ \"noise to image\", \"image generation\", \"unconditional generation\", \"data augmentation\" ], \"condition\": [], \"dataset\": [ \"INbreast\" ], \"augmentations\": [ \"crop and resize\", \"horizontal flip\", \"vertical flip\" ], \"generates\": [ \"calcification\", \"calcifications\", \"calcification roi\", \"calcification ROI\", \"calcification images\", \"calcification region of interest\" ], \"height\": 128, \"width\": 128, \"depth\": null, \"type\": \"DCGAN\", \"license\": \"MIT\", \"dataset_type\": \"public\", \"privacy_preservation\": null, \"tags\": [ \"Mammogram\", \"Mammography\", \"Digital Mammography\", \"Full field Mammography\", \"Full-field Mammography\", \"128x128\", \"128 x 128\", \"MammoGANs\", \"Microcalcification\", \"Microcalcifications\" ], \"year\": \"2021\" }, \"description\": { \"title\": \"DCGAN Model for Mammogram Calcification Region of Interest Generation (Trained on INbreast)\", \"provided_date\": \"12th May 2021\", \"trained_date\": \"May 2021\", \"provided_after_epoch\": 300, \"version\": \"0.0.1\", \"publication\": null, \"doi\": [ \"10.5281/zenodo.5187714\" ], \"comment\": \"A deep convolutional generative adversarial network (DCGAN) that generates regions of interest (ROI) of mammograms containing benign and/or malignant calcifications. Pixel dimensions are 128x128. The DCGAN was trained on ROIs from the INbreast dataset (Moreira et al, 2012). The uploaded ZIP file contains the files dcgan.pt (model weights), __init__.py (image generation method and utils), a README.md, and the GAN model architecture (in pytorch) below the /src folder. Kernel size=6 used in DCGAN discriminator.\" } } }</code></pre>","{\"references\": [\"Osuala, Richard et al. (2021). 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