18 citations · 27 across the 7 of their papers we have counts for
7 papers
Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging
Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl +6
Active learning (AL) has the potential to drastically reduce annotation costs in 3D biomedical image segmentation, where expert labeling of volumetric data is both time-consuming a…
nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation
Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl +6
Semantic segmentation is crucial for various biomedical applications, yet its reliance on large annotated datasets presents a bottleneck due to the high cost and specialized expert…
Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics
Lukas Klein, Carsten T. Lüth, Udo Schlegel +3
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 limit…
Overcoming Common Flaws in the Evaluation of Selective Classification Systems
Jeremias Traub, Till J. Bungert, Carsten T. Lüth +4
Selective Classification, wherein models can reject low-confidence predictions, promises reliable translation of machine-learning based classification systems to real-world scenari…
Understanding Silent Failures in Medical Image Classification
Till J. Bungert, Levin Kobelke, Paul F. Jaeger
To ensure the reliable use of classification systems in medical applications, it is crucial to prevent silent failures. This can be achieved by either designing classifiers that ar…
Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment
Carsten T. Lüth, Till J. Bungert, Lukas Klein +1
Active Learning (AL) aims to reduce the labeling burden by interactively selecting the most informative samples from a pool of unlabeled data. While there has been extensive resear…
A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification
Paul F. Jaeger, Carsten T. Lüth, Lukas Klein +1
Reliable application of machine learning-based decision systems in the wild is one of the major challenges currently investigated by the field. A large portion of established appro…