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Controllable and Efficient Multi-Class Pathology Nuclei Data Augmentation using Text-Conditioned Diffusion Models

Hyun-Jic Oh, Won-Ki Jeong

TL;DR

The paper tackles the data scarcity problem in nuclei segmentation and classification for digital pathology by introducing a two-stage diffusion-based data augmentation framework. It first generates multi-class nuclei labels and instance maps through a text-conditioned joint diffusion process, then synthesizes high-quality, label-consistent pathology images using a fine-tuned latent diffusion model (PathLDM) with a scalable conditioning mechanism. The approach achieves controllable label distributions and efficient image generation, yielding improved downstream segmentation and classification performance on diverse datasets (Lizard, PanNuke, EndoNuke) while reducing computational cost compared to prior diffusion methods. Overall, the method enhances data diversity and balance for robust pathology models and shows potential for scaling to larger histopathology images.

Abstract

In the field of computational pathology, deep learning algorithms have made significant progress in tasks such as nuclei segmentation and classification. However, the potential of these advanced methods is limited by the lack of available labeled data. Although image synthesis via recent generative models has been actively explored to address this challenge, existing works have barely addressed label augmentation and are mostly limited to single-class and unconditional label generation. In this paper, we introduce a novel two-stage framework for multi-class nuclei data augmentation using text-conditional diffusion models. In the first stage, we innovate nuclei label synthesis by generating multi-class semantic labels and corresponding instance maps through a joint diffusion model conditioned by text prompts that specify the label structure information. In the second stage, we utilize a semantic and text-conditional latent diffusion model to efficiently generate high-quality pathology images that align with the generated nuclei label images. We demonstrate the effectiveness of our method on large and diverse pathology nuclei datasets, with evaluations including qualitative and quantitative analyses, as well as assessments of downstream tasks.

Controllable and Efficient Multi-Class Pathology Nuclei Data Augmentation using Text-Conditioned Diffusion Models

TL;DR

The paper tackles the data scarcity problem in nuclei segmentation and classification for digital pathology by introducing a two-stage diffusion-based data augmentation framework. It first generates multi-class nuclei labels and instance maps through a text-conditioned joint diffusion process, then synthesizes high-quality, label-consistent pathology images using a fine-tuned latent diffusion model (PathLDM) with a scalable conditioning mechanism. The approach achieves controllable label distributions and efficient image generation, yielding improved downstream segmentation and classification performance on diverse datasets (Lizard, PanNuke, EndoNuke) while reducing computational cost compared to prior diffusion methods. Overall, the method enhances data diversity and balance for robust pathology models and shows potential for scaling to larger histopathology images.

Abstract

In the field of computational pathology, deep learning algorithms have made significant progress in tasks such as nuclei segmentation and classification. However, the potential of these advanced methods is limited by the lack of available labeled data. Although image synthesis via recent generative models has been actively explored to address this challenge, existing works have barely addressed label augmentation and are mostly limited to single-class and unconditional label generation. In this paper, we introduce a novel two-stage framework for multi-class nuclei data augmentation using text-conditional diffusion models. In the first stage, we innovate nuclei label synthesis by generating multi-class semantic labels and corresponding instance maps through a joint diffusion model conditioned by text prompts that specify the label structure information. In the second stage, we utilize a semantic and text-conditional latent diffusion model to efficiently generate high-quality pathology images that align with the generated nuclei label images. We demonstrate the effectiveness of our method on large and diverse pathology nuclei datasets, with evaluations including qualitative and quantitative analyses, as well as assessments of downstream tasks.
Paper Structure (12 sections, 7 equations, 3 figures, 3 tables)

This paper contains 12 sections, 7 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Background: Diffusion Models
  4. Text Conditional Multi-class Label Synthesis
  5. Conditional Image Synthesis with Pretrained LDM
  6. Experiment
  7. Datasets
  8. Implementation details
  9. Results
  10. Conclusion
  11. Acknowledgments.
  12. Disclosure of Interests.

Figures (3)

  • Figure 1: Overview of the proposed two-stage data synthesis framework, consisting of label and image synthesis steps. The framework utilize spatial text condition $c_{pr}$ for label synthesis (a). For image synthesis (b), we fine-tune the pretrained latent diffusion model with semantic condition $c_s$ and text condition $c_{pr}$.
  • Figure 2: Example of generated data using text conditions $c_{pr}$ (b). (a) shows the synthetic data by related works.
  • Figure 3: Graphical analysis. The unconditional model tends to replicate the high-frequency labels from the training data set (a), while the conditional model can generate labels with specific nucleus proportions (b). (c) shows that the nucleus proportion and class type conditions are effective for synthesizing the target label set. We also observe that it is 100% consistent for the given class types as (d).