{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/general-to-detailed-gan-for-infrequent-class","title":"General-to-Detailed GAN for Infrequent Class Medical Images","arxiv_id":"1812.01690","date":"2018-11-28","proceeding":null,"authors":["Tatsuki Koga","Naoki Nonaka","Jun Sakuma","Jun Seita"],"abstract":"Deep learning has significant potential for medical imaging. However, since\nthe incident rate of each disease varies widely, the frequency of classes in a\nmedical image dataset is imbalanced, leading to poor accuracy for such\ninfrequent classes. One possible solution is data augmentation of infrequent\nclasses using synthesized images created by Generative Adversarial Networks\n(GANs), but conventional GANs also require certain amount of images to learn.\nTo overcome this limitation, here we propose General-to-detailed GAN (GDGAN),\nserially connected two GANs, one for general labels and the other for detailed\nlabels. GDGAN produced diverse medical images, and the network trained with an\naugmented dataset outperformed other networks using existing methods with\nrespect to Area-Under-Curve (AUC) of Receiver Operating Characteristic (ROC)\ncurve.","url_abs":"http://arxiv.org/abs/1812.01690v1","url_pdf":"http://arxiv.org/pdf/1812.01690v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"general-to-detailed-gan-for-infrequent-class","repo_url":"https://github.com/seitalab/GDGAN.pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}