Nucleus Classification

Nucleus classification in histopathology images aims to automatically identify and categorize different cell types based on their nuclear morphology and spatial context, aiding in computer-aided cancer diagnosis. Recent research heavily utilizes deep learning, employing graph neural networks, transformers, and diffusion models to analyze both individual nuclei characteristics and their relationships within tissue structures. These advancements focus on improving accuracy, particularly for rare cell types, by incorporating techniques like multi-task learning, self-supervised pretraining, and inter-modality learning to leverage diverse data sources and address class imbalances. The resulting improvements in automated annotation and classification have significant implications for accelerating research and improving the precision of diagnostic pathology.