Artificial Intelligence for Digital and Computational Pathology

• DOI: 10.48550/arxiv.2401.06148
• Publication Date: 2024-01-01
• Authors: Andrew H. Song; Guillaume Jaume; Drew F. K. Willia...; Ming Y. Lu; Anurag Vaidya; Tiffany R. Miller; Faisal Mahmood
• Keywords: Digital Pathology; Modalities; Clinical Practice

Advances in digitizing tissue slides and the fast-paced progress artificial intelligence, including deep learning, have boosted field of computational pathology. This holds tremendous potential to automate clinical diagnosis, predict patient prognosis response therapy, discover new morphological biomarkers from images. Some these intelligence-based systems are now getting approved assist diagnosis; however, technical barriers remain for their widespread adoption integration as a research tool. Review consolidates recent methodological advances pathology predicting end points whole-slide images highlights how developments enable automation practice discovery biomarkers. We then provide future perspectives expands into broader range tasks with increasingly diverse modalities data.