Lukas Ruff
Impact in
- Artificial Intelligence top 2%
- Anomaly Detection Techniques and Applications
- Adversarial Robustness in Machine Learning
- Domain Adaptation and Few-Shot Learning
- Health Informatics top 10%
Papers in
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- Anomaly Detection Techniques and Applications 6
- AI in cancer detection 2
- Explainable Artificial Intelligence (XAI) 2
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- COVID-19 diagnosis using AI 2
- Radiomics and Machine Learning in Medical Imaging 1
- Co-authors
- Robert A. Vandermeulen (5 shared papers)Marius Kloft (5 shared papers)Lucas Deecke (3 shared papers)Shoaib Ahmed Siddiqui (1 shared paper)Emmanuel Müller (1 shared paper)Alexander Binder (1 shared paper)Stephan Mandt (1 shared paper)Grégoire Montavon (3 shared papers)
- Journals
- IEEE Transactions on Neural Networks and Learning Systems (1 paper)Nature Machine Intelligence (1 paper)Scientific Reports (1 paper)European Journal of Cancer (1 paper)Annual Review of Pathology Mechanisms of Disease (1 paper)
- Partner nations
- GermanySouth KoreaUnited Kingdom
In The Last Decade
Lukas Ruff
11 papers receiving 844 citations
Lukas Ruff's Hit Papers
Peers
Comparison fields: 5 of 99
- Artificial Intelligence 703
- Health Informatics 20
- Computer Networks and Communications 258
- Signal Processing 105
- Computer Vision and Pattern Recognition 146
Countries citing papers authored by Lukas Ruff
This map shows the geographic impact of Lukas Ruff's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lukas Ruff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lukas Ruff more than expected).
Fields of papers citing papers by Lukas Ruff
This network shows the impact of papers produced by Lukas Ruff. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lukas Ruff. The network helps show where Lukas Ruff may publish in the future.
Co-authors
The 25 scholars most cited alongside Lukas Ruff, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Deep One-Class Classification Hit paper breakdown → | 2018 | 538 |
| 2 | 2019 | 132 | |
| 3 | 2023 | 78 | |
| 4 | 2022 | 56 | |
| 5 | 2019 | 39 | |
| 6 | Explainable Deep One-Class Classification | 2021 | 9 |
| 7 | 2024 | 7 | |
| 8 | 2025 | 4 | |
| 9 | Transfer-Based Semantic Anomaly Detection | 2021 | 4 |
| 10 | 2024 | 3 | |
| 11 | 2024 | 1 | |
| 12 | 2023 | 0 |
About Lukas Ruff
Lukas Ruff is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Networks and Communications, Health Informatics and Signal Processing, having authored 12 papers that have together received 871 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (6 papers), Network Security and Intrusion Detection (2 papers), Artificial Intelligence in Healthcare and Education (2 papers), COVID-19 diagnosis using AI (2 papers), AI in cancer detection (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper) and Advanced Malware Detection Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (703 citations), Health Informatics (20 citations), Computer Networks and Communications (258 citations), Signal Processing (105 citations) and Computer Vision and Pattern Recognition (146 citations). Lukas Ruff has collaborated with scholars based in Germany, South Korea and United Kingdom. Frequent co-authors include Robert A. Vandermeulen, Marius Kloft, Lucas Deecke, Shoaib Ahmed Siddiqui, Emmanuel Müller, Alexander Binder, Stephan Mandt, Grégoire Montavon, Klaus‐Robert Müller and Wojciech Samek. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Nature Machine Intelligence, Scientific Reports, European Journal of Cancer and Annual Review of Pathology Mechanisms of Disease.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.