Digital Histopathology

Digital histopathology leverages deep learning to analyze digitized microscopic tissue slides, aiming to automate diagnosis and improve the efficiency of pathology workflows. Current research heavily utilizes multiple instance learning (MIL) and transformer-based architectures, often incorporating techniques like pre-training and stain normalization to address data heterogeneity and improve model generalizability across different datasets and staining protocols. This field is significant because it promises faster, more objective, and potentially more accurate diagnoses, ultimately impacting patient care and accelerating biomedical research through improved image analysis and quantitative assessment of tissue characteristics.