Single Cell Image

Single-cell image analysis focuses on extracting biological information from microscopic images of individual cells, aiming to improve diagnostics and accelerate biological discovery. Current research emphasizes developing robust and generalizable deep learning models, including vision transformers and self-supervised learning approaches like DINO, to address challenges such as batch effects, limited data, and varying image characteristics across different microscopy techniques. These advancements are improving the accuracy and reliability of cell classification, enabling applications in areas like hematology and cancer diagnosis, and facilitating the analysis of large-scale datasets generated by high-throughput screening methods. The development of channel-adaptive models and methods for generating pixel-level explanations further enhances the utility and trustworthiness of these computational tools.