Data imbalance is a classic problem in image classification, especially for medical images where normal data is much more than data with diseases. To make up for the absence of disease images, methods which can generate retinal OCT images with diseases from normal retinal images are investigated. Conditional GANs (cGAN) have shown significant success in natural images generation, but the applications for medical images are limited. In this work, we propose an end-to-end framework for OCT image generation based on cGAN. The new structural similarity index (SSIM) loss is introduced so that the model can take the structure-related details into consideration. In experiments, three kinds of retinal disease images are generated. The generated images assume the natural structure of the retina and thus are visually appealing. The method is further validated by testing the classification performance trained by the generated images.

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094924 (15 March 2019); doi: 10.1117/12.2510967

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)