Synthetic Medical Images from Dual Generative Adversarial Networks

2017/09/06 by John Guibas, John T. Guibas, Guibas, John T. +4 · 5 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #AI in cancer detection #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #cs.CV

paper · pdf · doi:10.48550/arxiv.1709.01872

First two authors contributed equally. Accepted to NIPS 2017 Workshops on Medical Imaging and Machine Learning for Health

openalex publication_date 2017/09/06 · arxiv created 2018/01/08 · arxiv updated 2018/01/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Currently there is strong interest in data-driven approaches to medical image classification. However, medical imaging data is scarce, expensive, and fraught with legal concerns regarding patient privacy. Typical consent forms only allow for patient data to be used in medical journals or education, meaning the majority of medical data is inaccessible for general public research. We propose a novel, two-stage pipeline for generating synthetic medical images from a pair of generative adversarial networks, tested in practice on retinal fundi images. We develop a hierarchical generation process to divide the complex image generation task into two parts: geometry and photorealism. We hope researchers will use our pipeline to bring private medical data into the public domain, sparking growth in imaging tasks that have previously relied on the hand-tuning of models. We have begun this initiative through the development of SynthMed, an online repository for synthetic medical images.

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