Controllable and Efficient Multi-Class Pathology Nuclei Data Augmentation using Text-Conditioned Diffusion Models
This work addresses data scarcity in computational pathology for tasks like nuclei segmentation and classification, representing an incremental improvement over existing methods by enabling multi-class and label-aware synthesis.
The paper tackles the problem of limited labeled data in computational pathology by introducing a two-stage framework for multi-class nuclei data augmentation using text-conditional diffusion models, resulting in high-quality pathology images that align with generated nuclei labels as demonstrated on large datasets.
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.