Minu D. Tizabi — accepted AI-conference papers

2024

Quality Assured: Rethinking Annotation Strategies in Imaging AI

Tim Rädsch*, Annika Reinke, Vivienn Weru, Minu D. Tizabi, Nicholas Heller, Fabian Isensee +2

ECCV 2024poster

"[width=1]figures/fig1l owr es.png Figure 1: Research Questions (RQs) tackled in this work. Based on 57,648 instance segmentation masks annotated by 924 annotators and 34 quality assurance (QA) workers from five different annotation providers, we (1) compared the effectiveness of generating high-qua…

Cited by 4 Source ↗PDF ↗Scholar ↗

2023

Why Is the Winner the Best?

Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu D. Tizabi, Fabian Isensee, Tim J. Adler +119

CVPR 2023poster

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and…

Cited by 29 Source ↗PDF ↗Scholar ↗