Guided Diffusion For Class-Conditioned Synthesis & Classification Of Microscopic Blood Cell Images

Microscopic visualization of diseased cells plays a vital role in the diagnosis and understanding of various medical conditions. Recent advances in deep learning generative models have shown remarkable potential as formidable tools for generating high-quality medical images. However, training these models generally requires large, annotated datasets, which are often costly and time-consuming to obtain. To overcome this challenge, we propose a fast-sampling guided score-based diffusion model with a classifier-free guidance strategy for class-conditioned generation of microscopic peripheral blood cell images. Our model achieves a Fréchet Inception Distance (FID) score of 10.24, demonstrating its ability to generate realistic synthetic blood cell images. Furthermore, our experimental results show that augmenting training data with these synthetic images significantly improves classification accuracy compared to relying solely on real data, highlighting the potential of synthetic data augmentation in hematology.