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10 December 2021 Processing multi-expert annotations in digital pathology: a study of the Gleason 2019 challenge
Adrien Foucart, Olivier Debeir, Christine Decaestecker
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Proceedings Volume 12088, 17th International Symposium on Medical Information Processing and Analysis; 120880X (2021) https://doi.org/10.1117/12.2604307
Event: Seventeenth International Symposium on Medical Information Processing and Analysis, 2021, Campinas, Brazil
Abstract
Deep learning algorithms rely on large amounts of annotations for learning and testing. In digital pathology, a ground truth is rarely available, and many tasks show large inter-expert disagreement. Using the Gleason2019 dataset, we analyse how the choices we make in getting the ground truth from multiple experts may affect the results and the conclusions we could make from challenges and benchmarks. We show that using undocumented consensus methods, as is often done, reduces our ability to properly analyse challenge results. We also show that taking into account each expert’s annotations enriches discussions on results and is more in line with the clinical reality and complexity of the application.
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Adrien Foucart, Olivier Debeir, and Christine Decaestecker "Processing multi-expert annotations in digital pathology: a study of the Gleason 2019 challenge", Proc. SPIE 12088, 17th International Symposium on Medical Information Processing and Analysis, 120880X (10 December 2021); https://doi.org/10.1117/12.2604307
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KEYWORDS
Pathology
Tissues
Image analysis
Statistical analysis
Prostate cancer
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