Ruff, Lukas

Deep Semi-Supervised Anomaly Detection

Ruff, L., Vandermeulen, R. A., Görnitz, N., Binder, A., Müller, E., Müller, K.-R., & Kloft, M. (2019) · arXiv

Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Cited by 388

Explainable Deep One-Class Classification

Liznerski, P., Ruff, L., Vandermeulen, R. A., Franks, B. J., Kloft, M., & Müller, K.-R. (2020) · arXiv

Anomaly Detection Techniques and ApplicationsExplainable Artificial Intelligence (XAI)

Cited by 185

RudolfV: A Foundation Model by Pathologists for Pathologists

Dippel, J., Feulner, B., Winterhoff, T., Milbich, T., Tietz, S., Schallenberg, S., Dernbach, G., Kunft, A., Heinke, S., Eich, M.-L., et al. (2024) · arXiv

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 27

Towards Robust Foundation Models for Digital Pathology

Kömen, J., de Jong, E. D., Hense, J., Marienwald, H., Dippel, J., Naumann, P., Marcus, E., Ruff, L., Alber, M., Teuwen, J., et al. (2025) · arXiv

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 10