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Claudia Winklmayr

2 accepted papers

2026

Better than Average: Spatially-Aware Aggregation of Segmentation Uncertainty Improves Downstream Performance

CVPR 2026

Uncertainty Quantification (UQ) is crucial for ensuring the reliability of automated image segmentations in safety-critical domains like biomedical image analysis or autonomous driving. In segmentation, UQ generates pixel-wise uncertainty scores that must be aggregated into image-level scores for do

Cited by 0SourcecodeScholar
2024

Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification

NeurIPS 2024poster

Uncertainty Quantification (UQ) is crucial for reliable image segmentation. Yet, while the field sees continual development of novel methods, a lack of agreed-upon benchmarks limits their systematic comparison and evaluation: Current UQ methods are typically tested either on overly simplistic toy da…