Synthetic Histopathology Image

Synthetic histopathology image generation aims to create realistic digital representations of tissue samples, addressing the limitations of acquiring and annotating large, diverse datasets for training AI models in digital pathology. Current research heavily utilizes diffusion models and generative adversarial networks (GANs), often incorporating transformers or convolutional neural networks, to generate images at various scales, from individual nuclei to whole slide images, sometimes conditioned on semantic masks or class labels. This technology offers significant potential for augmenting existing datasets, improving the performance of AI algorithms for disease classification and nuclei segmentation, enhancing data privacy, and facilitating educational applications in pathology.