Cell Annotation

Cell annotation, the process of identifying and labeling individual cells within microscopy images or single-cell sequencing data, is crucial for advancing biomedical research and diagnostics. Current research focuses on developing automated annotation methods using deep learning architectures like transformers, graph neural networks, and convolutional neural networks, often incorporating techniques like curriculum learning and contrastive learning to improve efficiency and accuracy, even with limited or imperfect annotations. These advancements are significantly impacting fields like pathology, oncology, and immunology by enabling faster, more objective, and reproducible analysis of complex biological data, ultimately improving diagnostic accuracy and therapeutic decision-making. The development of large, publicly available datasets and the exploration of weakly supervised and self-supervised learning approaches are also key themes.