ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis
This work addresses the need for high-quality synthetic histopathology images in digital pathology, where artifacts and staining variations are common, though it is incremental as it builds on existing diffusion and transformer techniques.
The authors tackled the problem of generating realistic histopathology images by introducing ViT-DAE, a method combining vision transformers and diffusion autoencoders, which outperformed GAN-based and vanilla DAE methods on three public datasets.
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.