Conditional diffusion models for synthetic histopathology image generation
Abstract
Artificial intelligence has shown great potential in computational pathology, but its progress is often limited by the scarcity and imbalance of annotated histopathology data. In particular, existing datasets frequently lack coverage of diverse pathological patterns, preventing the development of robust and generalizable models for fine-grained diagnostic tasks. To address this challenge, we propose a generative model for synthesizing high-resolution histopathology images conditioned on structural features. The proposed method builds on recent advances in diffusion-based generative modeling by combining Stable Diffusion with ControlNet, enabling spatially controlled image synthesis. Conditioning is performed using segmentation masks that encode cell structures and spatial distributions, allowing fine-grained control over the morphology and organization of synthetic tissue. The model is trained on a large-scale dataset comprising 3 million image patches from 19 tissue types scanned at 40x magnification, providing rich, high-resolution visual information. To evaluate the quality and utility of the generated images, we propose an evaluation pipeline incorporating both qualitative and quantitative analyses. Results demonstrate that the generated images are morphologically faithful and structurally consistent, supporting downstream tasks such as nuclei classification and segmentation.
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