Digital pathology uses high resolution scanners, image analysis algorithms and data infrastructure to transform traditional glass slides into whole slide images for viewing, quantification and sharing. Research in this field focuses on three intertwined areas: digitization, image analysis and clinical integration.

Work on digitization investigates how scanning resolution, color calibration and file formats affect diagnostic quality and downstream algorithm performance. Studies evaluate compression methods and storage architectures, since digital pathology generates very large datasets that must be archived, retrieved and shared efficiently and securely.

Image analysis research develops computational tools to segment tissue regions, detect and classify cells and quantify spatial patterns. Machine learning and deep learning models are trained to recognize tumor regions, grade cancers, estimate prognostic features and predict treatment response directly from tissue images. Validation studies compare automated measurements with expert pathologist assessments and patient outcomes to establish reliability and clinical relevance.

A major research theme is integrating digital workflows into routine pathology practice. This includes designing user interfaces for slide viewing, supporting remote consultation and second opinions, and enabling multidisciplinary collaboration through shared digital cases. Regulatory and quality assurance work addresses traceability, standardization of imaging protocols and algorithm validation in diverse clinical settings.

Future oriented research explores combining digital pathology with genomic, molecular and clinical data to build multimodal predictive models. There is also growing focus on explainable algorithms that highlight image regions driving decisions, to support trust and adoption. Overall, the research goal is to enhance diagnostic accuracy, reproducibility and efficiency while opening new avenues for quantitative, data driven pathology.