Digital Pathology

Digital pathology uses digitized microscopy images to analyze tissue samples, aiming to improve diagnostic accuracy and efficiency in healthcare. Current research focuses on developing and refining deep learning models, including transformers and convolutional neural networks, to address challenges like stain variation, limited annotated data, and the need for improved model interpretability and uncertainty quantification. These advancements are leading to more robust and efficient algorithms for tasks such as image segmentation, classification, and the integration of spatial transcriptomics data, ultimately impacting clinical workflows and potentially accelerating biomarker discovery.