Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers

• Published in: Modern pathology Vol. 36; no. 4; p. 100086
• Main Authors: Montezuma, Diana, Oliveira, Sara P., Neto, Pedro C., Oliveira, Domingos, Monteiro, Ana, Cardoso, Jaime S., Macedo-Pinto, Isabel
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2023
• Subjects: annotation; Artificial Intelligence; computational pathology; digital pathology; Humans; Machine Learning; Pathologists; Software; annotation; computational pathology; digital pathology; artificial intelligence
• ISSN: 0893-3952; 1530-0285; 1530-0285
• DOI: 10.1016/j.modpat.2022.100086

Training machine learning models for artificial intelligence (AI) applications in pathology often requires extensive annotation by human experts, but there is little guidance on the subject. In this work, we aimed to describe our experience and provide a simple, useful, and practical guide addressing annotation strategies for AI development in computational pathology. Annotation methodology will vary significantly depending on the specific study’s objectives, but common difficulties will be present across different settings. We summarize key aspects and issue guiding principles regarding team interaction, ground-truth quality assessment, different annotation types, and available software and hardware options and address common difficulties while annotating. This guide was specifically designed for pathology annotation, intending to help pathologists, other researchers, and AI developers with this process.