Label Free

Label-free techniques aim to extract information from images and data without relying on artificial labels or markers, leveraging inherent properties like autofluorescence or phase shifts. Current research focuses on developing deep learning models, including convolutional neural networks, transformers, and diffusion models, to analyze label-free data for tasks such as image segmentation, classification, and quantitative phase imaging. These methods offer significant advantages in various fields, including pathology, microbiology, and biomedical imaging, by reducing costs, increasing efficiency, and enabling analyses previously hindered by labeling limitations. The resulting advancements promise faster, cheaper, and more accessible diagnostic and analytical tools.