Leveraging Pre-trained Models for FF-to-FFPE Histopathological Image Translation
<|reference_start|>Leveraging Pre-trained Models for FF-to-FFPE Histopathological Image Translation: 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.<|reference_end|>
@article{zhang2024leveraging,
    title={Leveraging Pre-trained Models for FF-to-FFPE Histopathological Image
  Translation},
    author={Qilai Zhang, Jiawen Li, Peiran Liao, Jiali Hu, Tian Guan, Anjia Han
  and Yonghong He},
    journal={arXiv preprint arXiv:2406.18054},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18054},
    primaryClass={eess.IV cs.CV}
}