  Generative AI has received substantial attention in recent years due to its
ability to synthesize data that closely resembles the original data source.
While Generative Adversarial Networks (GANs) have provided innovative
approaches for histopathological image analysis, they suffer from limitations
such as mode collapse and overfitting in discriminator. Recently, Denoising
Diffusion models have demonstrated promising results in computer vision. These
models exhibit superior stability during training, better distribution
coverage, and produce high-quality diverse images. Additionally, they display a
high degree of resilience to noise and perturbations, making them well-suited
for use in digital pathology, where images commonly contain artifacts and
exhibit significant variations in staining. In this paper, we present a novel
approach, namely ViT-DAE, which integrates vision transformers (ViT) and
diffusion autoencoders for high-quality histopathology image synthesis. This
marks the first time that ViT has been introduced to diffusion autoencoders in
computational pathology, allowing the model to better capture the complex and
intricate details of histopathology images. We demonstrate the effectiveness of
ViT-DAE on three publicly available datasets. Our approach outperforms recent
GAN-based and vanilla DAE methods in generating realistic images.
