  The two primary types of Hematoxylin and Eosin (H&E) slides in histopathology
are Formalin-Fixed Paraffin-Embedded (FFPE) and Fresh Frozen (FF). FFPE slides
offer high quality histopathological images but require a labor-intensive
acquisition process. In contrast, FF slides can be prepared quickly, but the
image quality is relatively poor. Our task is to translate FF images into FFPE
style, thereby improving the image quality for diagnostic purposes. In this
paper, we propose Diffusion-FFPE, a method for FF-to-FFPE histopathological
image translation using a pre-trained diffusion model. Specifically, we employ
a one-step diffusion model as the generator and fine-tune it with LoRA adapters
using adversarial learning objectives. To ensure that the model effectively
captures both global structural information and local details, we propose a
multi-scale feature fusion (MFF) module. This module utilizes two VAE encoders
to extract features of varying image sizes and performs feature fusion before
feeding them into the UNet. Furthermore, we utilize a pre-trained
vision-language model for histopathology as the backbone for the discriminator
to further improve performance We conducted FF-to-FFPE translation experiments
on the TCGA-NSCLC datasets, and our method achieved better performance compared
to other methods. The code and models are released at
https://github.com/QilaiZhang/Diffusion-FFPE.
