18 citations · 29 across the 8 of their papers we have counts for
8 papers
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…
Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities
Kenza Amara, Lukas Klein, Carsten Lüth +3
The various limitations of Generative AI, such as hallucinations and model failures, have made it crucial to understand the role of different modalities in Visual Language Model (V…
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…
Enhancing predictive imaging biomarker discovery through treatment effect analysis
Shuhan Xiao, Lukas Klein, Jens Petersen +3
Identifying predictive covariates, which forecast individual treatment effectiveness, is crucial for decision-making across different disciplines such as personalized medicine. The…
Improving Explainability of Disentangled Representations using Multipath-Attribution Mappings
Lukas Klein, João B. S. Carvalho, Mennatallah El-Assady +3
Explainable AI aims to render model behavior understandable by humans, which can be seen as an intermediate step in extracting causal relations from correlative patterns. Due to th…
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…
From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process
Lukas Klein, Mennatallah El-Assady, Paul F. Jäger
Explainable AI (XAI) is a necessity in safety-critical systems such as in clinical diagnostics due to a high risk for fatal decisions. Currently, however, XAI resembles a loose col…