NASDM: Nuclei-Aware Semantic Histopathology Image Generation Using Diffusion Models

Understand

In recent years, computational pathology has seen tremendous progress driven by deep learning methods in segmentation and classification tasks aiding prognostic and diagnostic settings.

• Nuclei segmentation, for instance, is an important task for diagnosing different cancers.
• However, training deep learning models for nuclei segmentation requires large amounts of annotated data, which is expensive to collect and label.
• This necessitates explorations into generative modeling of histopathological images.