{"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/synthetic-medical-images-from-dual-generative","title":"Synthetic Medical Images from Dual Generative Adversarial Networks","arxiv_id":"1709.01872","date":"2017-09-06","proceeding":null,"authors":["John T. Guibas","Tejpal S. Virdi","Peter S. Li"],"abstract":"Currently there is strong interest in data-driven approaches to medical image\nclassification. However, medical imaging data is scarce, expensive, and fraught\nwith legal concerns regarding patient privacy. Typical consent forms only allow\nfor patient data to be used in medical journals or education, meaning the\nmajority of medical data is inaccessible for general public research. We\npropose a novel, two-stage pipeline for generating synthetic medical images\nfrom a pair of generative adversarial networks, tested in practice on retinal\nfundi images. We develop a hierarchical generation process to divide the\ncomplex image generation task into two parts: geometry and photorealism. We\nhope researchers will use our pipeline to bring private medical data into the\npublic domain, sparking growth in imaging tasks that have previously relied on\nthe hand-tuning of models. We have begun this initiative through the\ndevelopment of SynthMed, an online repository for synthetic medical images.","url_abs":"http://arxiv.org/abs/1709.01872v3","url_pdf":"http://arxiv.org/pdf/1709.01872v3.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":"synthetic-medical-images-from-dual-generative","repo_url":"https://github.com/HarshaVardhanVanama/Synthetic-Medical-Images","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"medical-image-classification","task_name":"Medical Image Classification"},{"task_slug":"medical-image-generation","task_name":"Medical Image Generation"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1709.01872","atlas_url":"https://app.syntology.ai/?focus=1709.01872","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}