Diagnosability of Synthetic Retinal Fundus Images for Plus Disease Detection in Retinopathy of Prematurity

Aaron S. Coyner, Jimmy Chen, J. Peter Campbell, Susan Ostmo, Praveer Singh, Jayashree Kalpathy-Cramer, Michael F. Chiang

Research output: Contribution to journalArticlepeer-review

Abstract

Advances in generative adversarial networks have allowed for engineering of highly-realistic images. Many studies have applied these techniques to medical images. However, evaluation of generated medical images often relies upon image quality and reconstruction metrics, and subjective evaluation by laypersons. This is acceptable for generation of images depicting everyday objects, but not for medical images, where there may be subtle features experts rely upon for diagnosis. We implemented the pix2pix generative adversarial network for retinal fundus image generation, and evaluated the ability of experts to identify generated images as such and to form accurate diagnoses of plus disease in retinopathy of prematurity. We found that, while experts could discern between real and generated images, the diagnoses between image sets were similar. By directly evaluating and confirming physicians' abilities to diagnose generated retinal fundus images, this work supports conclusions that generated images may be viable for dataset augmentation and physician training.

Original languageEnglish (US)
Pages (from-to)329-337
Number of pages9
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2020
StatePublished - 2020

ASJC Scopus subject areas

  • Medicine(all)

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