18 citations · 27 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…