SpaDiffHis: Sparse-point Guided Diffusion for Histopathology Image Synthesis with Contrastive Learning

Area of Science:

• Digital Pathology
• Computational Imaging
• Artificial Intelligence in Medicine

Background:

• Generating realistic synthetic histopathology images is crucial for training AI models and overcoming data limitations.
• Existing methods often rely on text prompts, which may not fully capture complex spatial and structural information.
• There's a need for methods that ensure structural integrity and class balance in synthetic data.

Purpose of the Study:

• To develop a novel framework for generating realistic synthetic histopathology images and corresponding segmentation masks.
• To leverage spatial information and contrastive learning for improved fidelity and nucleus structure preservation.
• To provide high-quality synthetic data that facilitates downstream tasks like segmentation and classification.

Main Methods:

• Fine-tuning a Stable Diffusion model conditioned on labeled point maps.
• Utilizing unsupervised nuclei detection for spatial information.
• Incorporating contrastive learning with a contrastive head to align latent representations with domain-specific characteristics.
• Ensuring nucleus structure preservation and class balance.

Main Results:

• The SpaDiffHis pipeline generates high-quality synthetic hematoxylin and eosin (H&E) patches with improved fidelity and stain consistency.
• The method produces synthetic images with fidelity and diversity comparable to real histopathology slides (e.g., FID scores in the range of 33-36).
• Co-synthesized segmentation masks achieve high Dice metrics (up to 0.857) on benchmark datasets (Lizard, PanNuke, CoNSeP).

Conclusions:

• The proposed diffusion-based synthesis approach effectively generates realistic nuclei morphology and structurally sound tissue characteristics.
• SpaDiffHis offers a powerful tool for creating diverse and high-fidelity synthetic histopathology data.
• The generated synthetic data and masks can significantly aid in the development and validation of computational pathology tools.