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Carsten T. Lüth

4 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

Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics

NeurIPS 2024poster

Explainable AI (XAI) is a rapidly growing domain with a myriad of proposed methods as well as metrics aiming to evaluate their efficacy. However, current studies are often of limited scope, examining only a handful of XAI methods and ignoring underlying design parameters for performance, such as the…

2024

Overcoming Common Flaws in the Evaluation of Selective Classification Systems

NeurIPS 2024spotlight

Selective Classification, wherein models can reject low-confidence predictions, promises reliable translation of machine-learning based classification systems to real-world scenarios such as clinical diagnostics. While current evaluation of these systems typically assumes fixed working points based…

2024

ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation

ICLR 2024oral

Uncertainty estimation is an essential and heavily-studied component for the reliable application of semantic segmentation methods. While various studies exist claiming methodological advances on the one hand, and successful application on the other hand, the field is currently hampered by a gap bet…