Annotation Practices in Computational Pathology: A European Society of Digital and Integrative Pathology (ESDIP) Survey Study
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.
Authors
- Vincenzo L’Imperio (ORCID: https://orcid.org/0000-0002-9284-2998)
- Vincenzo Della Mea (ORCID: https://orcid.org/0000-0002-0144-3802)
- Rita Carvalho (ORCID: https://orcid.org/0000-0003-3714-7527)
- Peter Boor (ORCID: https://orcid.org/0000-0001-9921-4284)
- David Ameisen (ORCID: https://orcid.org/0000-0001-9232-208X)
- Sara P. Oliveira (ORCID: https://orcid.org/0000-0002-6586-9079)
- Yuri Tolkach (ORCID: https://orcid.org/0000-0001-5239-2841)
- Mustafa Yousif
- Mircea-Sebastian Şerbănescu (ORCID: https://orcid.org/0000-0002-1824-6354)
- Tim‐Rasmus Kiehl (ORCID: https://orcid.org/0000-0003-0616-7082)
- Norman Zerbe (ORCID: https://orcid.org/0000-0002-0314-3037)
- Sabine Leh (ORCID: https://orcid.org/0000-0002-0764-2941)
- Diana Montezuma (ORCID: https://orcid.org/0000-0001-9551-4589)
- Alex Haragan
Institutions
- University of Udine (IT)
- University of Liverpool (GB)
- Dutch Cancer Society (NL)
- European Society for Medical Oncology (CH)
- Haukeland University Hospital (NO)
- Royal Liverpool University Hospital (GB)
- University of Medicine and Pharmacy of Craiova (RO)
- European Society of Cardiology (FR)
- Humboldt-Universität zu Berlin (DE)
- Helios Klinikum Emil von Behring (DE)
- Instituto Português de Oncologia Francisco Gentil (PT)
- IPO Porto (PT)
- University Hospital Cologne (DE)
- University of Milano-Bicocca (IT)
- RWTH Aachen University (DE)
Publication Details
- Journal
- Laboratory Investigation
- Published
- 2024-11-29
- DOI
- https://doi.org/10.1016/j.labinv.2024.102203
- Citations
- 6
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 2.08