Representative Data Generation of Diabetic Retinopathy Synthetic Retinal Images

Authors Wietse ten Dam, Meike Grol, Zelda Zeegers, Alireza Dehghani, Huib Aldewereld
Published in HCAIep ’23, December 14–15, 2023, Dublin, Ireland
Publication date 2023
Research groups Artificial Intelligence
Type Article


Machine learning models have proven their use in the medical field, assisting physicians in early diagnosis of serious diseases. Diabetic retinopathy is one of such diseases that could benefit from early detection, but the amount of data available to train accurate models is limited. This challenge could be tackled through the use of syn thetic data in the training of machine learning models. This paper proposes a novel procedure for the generation of synthetic data-sets for diabetic retinopathy that accurately reflect the characteristics and complexities of real-world medical data-sets. By employing a systematic literature review in combination with an expert study, this research aims to improve the data generation that underpins the performance and reliability of AI systems for diabetic retinopa thy detection, enhancing early detection and treatment accessibility. Special attention is paid to inherent, human-centered issues in the synthetic data generation to mitigate representation risks.

On this publication contributed

Language English
Published in HCAIep ’23, December 14–15, 2023, Dublin, Ireland
Key words etic retinopathy, representative synthetic data generation, Medical GAN, representation risks
Digital Object Identifier 10.1145/3633083.3633175

Artificial Intelligence