USING GENERATIVE MODELS TO CREATE SYNTHETIC MEDICAL IMAGING DATA TO BOOST MODEL PERFORMANCE ON SPARSE DEMOGRAPHIC GROUPS

Abstract

Recent advancements in deep generative models, specifically Generative Adversarial Networks (GANs) and Diffusion Models, have introduced new methods for generating synthetic medical imaging data. These models can produce highly realistic images, particularly for underrepresented demographic groups, thereby enhancing the diversity of existing medical datasets without incur- ring significant data collection costs. This research utilizes GANs and Diffusion Models to generate synthetic medical images, aiming to address biases in diagnostic models towards majority pop- ulations. The study assesses the impact of these augmented datasets on the performance of a conventional Convolutional Neural Network (CNN) by comparing its classification accuracy on the original imbalanced dataset with that on a dataset enhanced with synthetic images. Specifically, the performance metrics focus on the accuracy of classifying conditions such as edema, no find- ings, and pneumonia across combined minority groups versus each majority demographic group. The results from experimentation demonstrated that CNNs trained on the supplemented datasets did not achieve a higher Area Under the Receiver Operating Characteristic (AUROC) curve score compared to those trained on the original dataset. We fail to reject the null hypothesis and can- not say that the CNNs trained on the supplemented data have a significant difference in AUROC performance.