| Guided image generation with conditional invertible neural networks L Ardizzone, C Lüth, J Kruse, C Rother, U Köthe arXiv preprint arXiv:1907.02392, 2019 | 354* | 2019 |
| A call to reflect on evaluation practices for failure detection in image classification PF Jaeger, CT Lüth, L Klein, TJ Bungert International Conference on Learning Representations (ICLR), 2023, 2022 | 39 | 2022 |
| Navigating the pitfalls of active learning evaluation: A systematic framework for meaningful performance assessment C Lüth, T Bungert, L Klein, PF Jaeger Conference on Neural Information Processing Systems (NeurIPS), 2023, 2023 | 14* | 2023 |
| ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation KC Kahl*, CT Lüth*, M Zenk, K Maier-Hein, PF Jaeger, ... International Conference on Learning Representations (ICLR), 2024, 2024 | 11 | 2024 |
| Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT SD Almeida, T Norajitra, CT Lüth, T Wald, V Weru, M Nolden, PF Jäger, ... European radiology 34 (7), 4379-4392, 2024 | 6 | 2024 |
| cOOpD: reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations SD Almeida*, CT Lüth*, T Norajitra, T Wald, M Nolden, PF Jäger, ... International Conference on Medical Image Computing and Computer-Assisted …, 2023 | 5 | 2023 |
| Cradl: Contrastive representations for unsupervised anomaly detection and localization CT Lüth, D Zimmerer, G Koehler, PF Jaeger, F Isensee, J Petersen, ... arXiv preprint arXiv:2301.02126, 2023 | 5 | 2023 |
| Unsupervised Anomaly Detection in the Wild D Zimmerer, D Paech, C Lüth, J Petersen, G Köhler, K Maier-Hein Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on …, 2022 | 2 | 2022 |
| Embarrassingly Simple Scribble Supervision for 3D Medical Segmentation K Gotkowski, C Lüth, PF Jäger, S Ziegler, L Krämer, S Denner, S Xiao, ... arXiv preprint arXiv:2403.12834, 2024 | 1 | 2024 |
| Capturing COPD heterogeneity: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography SD Almeida, T Norajitra, CT Lüth, T Wald, V Weru, M Nolden, PF Jäger, ... Frontiers in Medicine 11, 1360706, 2024 | 1 | 2024 |
| Contrastive representations for unsupervised anomaly detection and localization CT Lüth, D Zimmerer, G Koehler, PF Jaeger, F Isenensee, KH Maier-Hein BVM Workshop, 246-252, 2023 | 1 | 2023 |
| Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities K Amara, L Klein, C Lüth, P Jäger, H Strobelt, M El-Assady arXiv preprint arXiv:2410.01690, 2024 | | 2024 |
| Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics L Klein, CT Lüth, U Schlegel, TJ Bungert, M El-Assady, PF Jäger NeurIPS 2024 (Benchmark), 2024 | | 2024 |
| How do deep-learning models generalize across populations? Cross-ethnicity generalization of COPD detection S D. Almeida, T Norajitra, CT Lüth, T Wald, V Weru, M Nolden, PF Jäger, ... Insights into imaging 15 (1), 198, 2024 | | 2024 |
| Overcoming Common Flaws in the Evaluation of Selective Classification Systems J Traub, TJ Bungert, CT Lüth, M Baumgartner, KH Maier-Hein, ... NeurIPS 2024 (Spotlight), 2024 | | 2024 |
| Fresh Perspectives on Lung Morphometry and Pulmonary Drug Delivery: AI-powered 3D Imaging in Healthy and Fibrotic Murine Lungs L Yang, P Kumar, Q Liu, P Chen, C Li, C Lüth, L Krämer, C Gabriel, ... B30. SCARRED FOR LIFE: TRANSLATIONAL RESEARCH IN INTERSTITIAL ABNORMALITIES …, 2024 | | 2024 |
| Reformulating COPD Classification on Chest CT Scans as Anomaly Detection using Contrastive Representations: cOOpD SD Almeida, CT Lüth, T Norajitra, T Wald, M Nolden, PF Jäger, ... BVM Workshop, 115-115, 2024 | | 2024 |
| Probabilistic modeling of COPD imaging characteristics for disease severity prediction SD Almeida, T Norajitra, C Lueth, T Wald, T Kopytova, M Nolden, ... European Respiratory Journal 60 (suppl 66), 2022 | | 2022 |
| LATEC—A benchmark for large-scale attribution & attention evaluation in computer vision L Klein, U Schlegel, TJ Bungert, CT Lüth, M El-Assady, PF Jaeger | | |
Paul F. JaegerInteractive Machine Learning Research Group at DKFZ在 dkfz.de 的电子邮件经过验证
