Annotation Practices in Computational Pathology: A European Society of Digital and Integrative Pathology (ESDIP) Survey Study
Year: 2024
Type: article
Study design: Observational Study
Abstract: Integrating digital pathology and artificial intelligence (AI) algorithms can potentially improve diagnostic practice and precision medicine. Developing reliable, generalizable, and comparable AI algorithms depends on access to meticulously annotated data. However, achieving this requires robust collaboration among pathologists, computer scientists, and other researchers to ensure data quality and consistency. The lack of standardization and scalability is a significant challenge when generating annotations and annotated data sets. Recognizing these limitations, the Scientific Committee of the European Society of Digital and Integrative Pathology (ESDIP) performed a comprehensive international survey to understand the current state of annotation practices and identify actionable areas to address critical needs in the annotation process. The analysis and summary of the survey results provide several insights for all stakeholders involved in data preparation and ground truthing, ultimately contributing to the advancement of AI in computational pathology.
Source: Laboratory Investigation
Institutions: Universidade do Porto, Instituto Português de Oncologia Francisco Gentil, IPO Porto, Centro de Investigação do Instituto Português de Oncologia do Porto Francisco Gentil, IMP Diagnostics (Portugal), RISE - Health Research Network, The Netherlands Cancer Institute, University Hospital Cologne, Universitätsklinikum Aachen, RWTH Aachen University, Royal Liverpool University Hospital, Helios Klinikum Emil von Behring, University of Udine, Humboldt-Universität zu Berlin, Freie Universität Berlin, Charité - Universitätsmedizin Berlin, Haukeland University Hospital, University of Bergen, University of Michigan, Michigan Medicine, University of Medicine and Pharmacy of Craiova, University of Milano-Bicocca
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,480
OpenAlex ID: W4404870204