Enhancing Diagnostic Accuracy Through Generative Ai And Synthetic Data Generation For Robust Medical Imaging

Abstract

The scarcity, heterogeneity, and limited availability of high-quality annotated medical imaging data
continue to pose a significant challenge in developing robust and generalizable deep learning models for
clinical applications. In real-world healthcare scenarios, acquiring large-scale labeled datasets is difficult due
to privacy regulations, high annotation costs, and the requirement of expert radiologist involvement. These
limitations often result in models that lack generalization and perform poorly on unseen or rare disease cases.
To address this issue, this project proposes a Multi-Domain Generative Adversarial Network (MD-GAN)
framework designed specifically for cross-organ synthetic medical image synthesis. The proposed system is
trained on multiple imaging modalities, including Brain MRI and Lung CT/X-ray datasets, enabling it to learn
both shared anatomical structures and modality-specific pathological variations. The architecture incorporates
a shared feature extraction backbone that captures common medical patterns across domains, while organspecific
conditioning modules ensure that unique disease characteristics are preserved and accurately
represented. Through adversarial training between the generator and discriminator, the model produces highly
realistic synthetic medical images that can effectively augment existing datasets. Experimental results
demonstrate that incorporating MD-GAN-generated synthetic data significantly enhances downstream medical
image segmentation performance, particularly improving the Dice Similarity Coefficient (DSC) when compared
to models trained exclusively on real-world data. This confirms the effectiveness of the proposed approach in
improving diagnostic accuracy and model robustness in medical imaging applications.