OptiPathD: A Capacity-Optimized Diffusion Foundation Model for Pathology Image Generation

Generative models hold promise in addressing data scarcity and imbalance in computational pathology, yet current approaches often suffer from limited generalization due to either overfitting on narrow domains or reliance on pre-trained models from unrelated natural image distributions. In this work, we introduce OptiPathD, the first pathology-specific generative foundation model optimized for scalable and generalizable image synthesis. Leveraging our curated dataset CPIA comprising over 148 million multi-scale, multi-organ whole-slide image patches, we pre-train a transformer-based diffusion model with pathology-aware design. To enhance both fidelity and generalization, we propose a principled capacity optimization strategy that aligns model complexity with data scale. Extensive evaluations demonstrate that OptiPathD achieves state-of-the-art performance in conditional image generation, outperforming present generative models across fidelity, diversity, and transferability metrics. Further experiments using downstream classification task on ROSE dataset confirm the efficacy of our generated images. Our work provides a foundation for generative pathology modeling, offering a scalable, domain-specialized, and transferable solution to support data-driven clinical research and diagnostic applications.