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Leveraging Pre-trained Models for FF-to-FFPE Histopathological Image Translation

Qilai Zhang, Jiawen Li, Peiran Liao, Jiali Hu, Tian Guan, Anjia Han, Yonghong He

TL;DR

This paper proposes Diffusion-FFPE, a method for FF-to-FFPE histopathological image translation using a pre-trained diffusion model, which utilizes a one-step diffusion model as the generator, and introduces a multi-scale feature fusion module that leverages two VAE encoders to extract features at different image resolutions.

Abstract

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 utilize a one-step diffusion model as the generator, which we fine-tune using LoRA adapters within an adversarial learning framework. To enable the model to effectively capture both global structural patterns and local details, we introduce a multi-scale feature fusion module that leverages two VAE encoders to extract features at different image resolutions, performing feature fusion before inputting them into the UNet. Additionally, a pre-trained vision-language model for histopathology serves as the backbone for the discriminator, enhancing model performance. Our FF-to-FFPE translation experiments on the TCGA-NSCLC dataset demonstrate that the proposed approach outperforms existing methods. The code and models are released at https://github.com/QilaiZhang/Diffusion-FFPE.

Leveraging Pre-trained Models for FF-to-FFPE Histopathological Image Translation

TL;DR

This paper proposes Diffusion-FFPE, a method for FF-to-FFPE histopathological image translation using a pre-trained diffusion model, which utilizes a one-step diffusion model as the generator, and introduces a multi-scale feature fusion module that leverages two VAE encoders to extract features at different image resolutions.

Abstract

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 utilize a one-step diffusion model as the generator, which we fine-tune using LoRA adapters within an adversarial learning framework. To enable the model to effectively capture both global structural patterns and local details, we introduce a multi-scale feature fusion module that leverages two VAE encoders to extract features at different image resolutions, performing feature fusion before inputting them into the UNet. Additionally, a pre-trained vision-language model for histopathology serves as the backbone for the discriminator, enhancing model performance. Our FF-to-FFPE translation experiments on the TCGA-NSCLC dataset demonstrate that the proposed approach outperforms existing methods. The code and models are released at https://github.com/QilaiZhang/Diffusion-FFPE.

Paper Structure

This paper contains 14 sections, 9 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. The Generator with a Pre-trained Diffusion Model
  4. The Multi-scale Feature Fusion Module
  5. The Discriminator with a Pre-trained Vision Model
  6. Adversarial Learning Objective
  7. Experiments
  8. Datasets and Implementation Details
  9. Comparison Experiments
  10. Ablation Study
  11. The Impact of Pre-trained Weights in Generator
  12. The Impact of Discirminator
  13. The Impact of Multi-scale Feature Fusion Module
  14. Conclusion

Figures (3)

  • Figure 1: Overview of Diffusion-FFPE. During training, the generator's weights are fixed, with trainable LoRA adapters added to each of its components. The discriminator utilizes a pre-trained vision model as its backbone, followed by a trainable classifier. Intermediate features, fused by the MFF module from both the global and local VAE encoders, are forwarded to the VAE decoder through skip connections.
  • Figure 2: Visualization results of comparison experiments.
  • Figure 3: Visualization results of ablation study.