activity
20222024
most citedLabeling instructions matter in biomedical image analysis

3 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ME2024

Variable selection via fused sparse-group lasso penalized multi-state models incorporating molecular data

Kaya Miah, Jelle J. Goeman, Hein Putter +2

In multi-state models based on high-dimensional data, effective modeling strategies are required to determine an optimal, ideally parsimonious model. In particular, linking covaria…

cs.CV20242 cited

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:…

cs.CV20231 cited

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…

stat.ME2023

Simulating and reporting frequentist operating characteristics of clinical trials that borrow external information

Annette Kopp-Schneider, Manuel Wiesenfarth, Leonhard Held +1

Borrowing of information from historical or external data to inform inference in a current trial is an expanding field in the era of precision medicine, where trials are often perf…

cs.CV20223 cited

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…