Back to articles
Articles
Volume: 33 | Article ID: art00003
Image
Controllable Medical Image Generation via Generative Adversarial Networks
  DOI :  10.2352/ISSN.2470-1173.2021.11.HVEI-112  Published OnlineJanuary 2021
Abstract

Radiologists and pathologists frequently make highly consequential perceptual decisions. For example, visually searching for a tumor and recognizing whether it is malignant can have a life-changing impact on a patient. Unfortunately, all human perceivers— even radiologists—have perceptual biases. Because human perceivers (medical doctors) will, for the foreseeable future, be the final judges of whether a tumor is malignant, understanding and mitigating human perceptual biases is important. While there has been research on perceptual biases in medical image perception tasks, the stimuli used for these studies were highly artificial and often critiqued. Realistic stimuli have not been used because it has not been possible to generate or control them for psychophysical experiments. Here, we propose to use Generative Adversarial Networks (GAN) to create vivid and realistic medical image stimuli that can be used in psychophysical and computer vision studies of medical image perception. Our model can generate tumor-like stimuli with specified shapes and realistic textures in a controlled manner. Various experiments showed the authenticity of our GAN-generated stimuli and the controllability of our model.

Subject Areas :
Views 91
Downloads 17
 articleview.views 91
 articleview.downloads 17
  Cite this article 

Zhihang Ren, Stella X. Yu, David Whitney, "Controllable Medical Image Generation via Generative Adversarial Networksin Proc. IS&T Int’l. Symp. on Electronic Imaging: Human Vision and Electronic Imaging,  2021,  pp 112-1 - 112-6,  https://doi.org/10.2352/ISSN.2470-1173.2021.11.HVEI-112

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2021
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA