2021/09/02 by Aakash Saboo, Sai Niranjan Ramachandran, Saboo, Aakash +5
Computer Science · #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2109.01071
openalex publication_date 2021/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Disease-aware image editing by means of generative adversarial networks (GANs) constitutes a promising avenue for advancing the use of AI in the healthcare sector. Here, we present a proof of concept of this idea. While GAN-based techniques have been successful in generating and manipulating natural images, their application to the medical domain, however, is still in its infancy. Working with the CheXpert data set, we show that StyleGAN can be trained to generate realistic chest X-rays. Inspired by the Cyclic Reverse Generator (CRG) framework, we train an encoder that allows for faithfully inverting the generator on synthetic X-rays and provides organ-level reconstructions of real ones. Employing a guided manipulation of latent codes, we confer the medical condition of cardiomegaly (increased heart size) onto real X-rays from healthy patients. This work was presented in the Medical Imaging meets Neurips Workshop 2020, which was held as part of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020) in Vancouver, Canada