MedNet and U$^2$-ViT: A Nested Vision Transformer Diffusion Framework for Diverse Medical Images Generation

Abstract

The scarcity of diverse, high-quality medical images remains a significant bottleneck in advancing AI-driven medical diagnostics and treatment planning. Traditional datasets often suffer from class imbalances, limited disease coverage, and insufficient variations in imaging conditions - challenges that hinder the development of robust, generalizable models. To address this, we introduce MedNet, a large-scale, multi-modal text-to-image dataset spanning 61 disease categories with 160 classes, covering both humans and animals. We built this with images from trusted public repositories, ensuring data diversity, quality, and ethical compliance. Additionally, we propose U$^2$-ViT, a novel diffusion-based text-to-image generation architecture arranging Vision Transformers (ViTs) in a nested U-Net structure to enhance the generation of diverse, and high-fidelity medical images. Through extensive experiments, we demonstrate that our model achieves the best Fr'echet Inception Distance (FID) score compared to state-of-the-art models. The source code is available at https://github.com/LakshmikarPolamreddy/MedNet-U2-ViT.