Cell Image

Cell image analysis focuses on extracting meaningful information from microscopic images of cells, primarily for biomedical research and diagnostics. Current research emphasizes developing robust and efficient algorithms, often leveraging deep learning architectures like transformers, U-Nets, and GANs, to address challenges such as cell segmentation, tracking, and classification across diverse imaging modalities and conditions. These advancements are crucial for high-throughput screening, automated analysis of large datasets, and improved accuracy in diagnosing diseases based on cellular morphology and behavior. Furthermore, active learning and data augmentation techniques are being explored to reduce the reliance on extensive manual annotation, making these powerful tools more accessible.