Pathology Workflow

Pathology workflow research focuses on automating and improving the analysis of digital whole slide images (WSIs) to accelerate and enhance diagnostic accuracy. Current efforts concentrate on developing and benchmarking deep learning models, including transformers and convolutional neural networks, for tasks such as stain normalization, tissue segmentation (nuclei, glands), and biomarker prediction from H&E stained images, often employing weakly supervised learning techniques. These advancements aim to streamline the diagnostic process, reduce inter-observer variability, and enable more efficient and objective quantification of tissue features, ultimately improving patient care and accelerating research in precision oncology.