Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers
2023; Elsevier BV; Volume: 36; Issue: 4 Linguagem: Inglês
10.1016/j.modpat.2022.100086
ISSN1530-0285
AutoresDiana Montezuma, Sara P. Oliveira, Pedro C. Neto, Domingos Sávio Ferreira de Oliveira, Ana Raquel Monteiro, Jaime S. Cardoso, Isabel Macedo-Pinto,
Tópico(s)Radiomics and Machine Learning in Medical Imaging
ResumoTraining 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.
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