Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning algorithms into the laboratory information system
M. Angeloni, D. Rizzi, S. Schoen, A. Caputo, F. Merolla, A. Hartmann, F. Ferrazzi, F. Fraggetta
This paper presents a framework for integrating deep-learning models into the laboratory information system using open-source tools and HL7 standards to support clinical adoption in digital pathology.
bioRxiv · Jul 11, 2024Pathology