Nucleus Annotation

Nucleus annotation in digital pathology focuses on automatically identifying, segmenting, and classifying cell nuclei within microscopic images, aiming to improve efficiency and accuracy in disease diagnosis and research. Current research emphasizes developing robust deep learning models, including variations of U-Net architectures and transformer-based approaches, often incorporating self-supervised learning and techniques like masked image modeling or privileged knowledge distillation to leverage limited annotated data and improve generalization across diverse image types and staining protocols. These advancements hold significant potential for accelerating the analysis of histopathology images, enabling more efficient and precise quantification of cellular features for applications in cancer diagnostics and other fields of biomedical research.