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

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Montezuma, D (Author) Other; Oliveira, SP (Author) Other; Neto, PC (Author) Other; Oliveira, D (Author) Other; Monteiro, A (Author) Other; Jaime S Cardoso (Author) FEUP; Macedo-Pinto, I (Author) Other

Journal

Title: Modern Pathology

Vol. 36

ISSN: 0893-3952

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00Y-34Q

DOI: 10.1016/j.modpat.2022.100086

Abstract (EN): 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.(c) 2022 THE AUTHORS. Published by Elsevier Inc. on behalf of the United States & Canadian Academy of Pathology. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9