17 citations · 23 across the 6 of their papers we have counts for
4 papers · 1 filter
Quality Assured: Rethinking Annotation Strategies in Imaging AI
Tim Rädsch, Annika Reinke, Vivienn Weru +5
This paper does not describe a novel method. Instead, it studies an essential foundation for reliable benchmarking and ultimately real-world application of AI-based image analysis:…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Labeling instructions matter in biomedical image analysis
Tim Rädsch, Annika Reinke, Vivienn Weru +7
Biomedical image analysis algorithm validation depends on high-quality annotation of reference datasets, for which labeling instructions are key. Despite their importance, their op…