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20222025
most citedA Call to Reflect on Evaluation Practices for Failure Detection in Image Classification

18 citations · 29 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2025

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…

cs.CV2024★ 4 cited

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…

cs.CV2023★ 2 cited

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…

cs.CV2023★ 3 cited

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…

cs.CV2022★ 18 cited

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…

cs.CV2022★ 1 cited

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…