Lukas Ruff
- Artificial Intelligence top 2%
- Anomaly Detection Techniques and Applications 5
- AI in cancer detection 3
- Explainable Artificial Intelligence (XAI) 2
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education 2
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- Network Security and Intrusion Detection 2
- Signal Processing top 10%
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- Radiomics and Machine Learning in Medical Imaging 2
- COVID-19 diagnosis using AI 2
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- Cholangiocarcinoma and Gallbladder Cancer Studies 1
- Co-authors
- Robert A. VandermeulenMarius KloftLucas DeeckeAlexander BinderShoaib Ahmed SiddiquiEmmanuel MüllerGrégoire MontavonKlaus‐Robert Müller
- Journals
- Scientific Reports (1 paper)European Journal of Cancer (1 paper)Nature Machine Intelligence (1 paper)
- Partner nations
- GermanySouth KoreaUnited Kingdom
In The Last Decade
Lukas Ruff
10 papers receiving 619 citations
Hit Papers
Peers
Comparison fields: 5 of 96
- Artificial Intelligence 518
- Health Informatics 17
- Computer Networks and Communications 193
- Signal Processing 83
- Computer Vision and Pattern Recognition 93
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-authorship network
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 | 2025 | 2 | |
| 2 | 2024 | 4 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 1 | |
| 5 | 2023 | 57 | |
| 6 | 2022 | 44 | |
| 7 | Explainable Deep One-Class Classification | 2021 | 9 |
| 8 | Transfer-Based Semantic Anomaly Detection | 2021 | 4 |
| 9 | 2019 | 34 | |
| 10 | Deep One-Class Classificationbreakdown → | 2018 | 484 |
About Lukas Ruff
Lukas Ruff is a scholar working on Health Informatics, Artificial Intelligence, Signal Processing, Radiology, Nuclear Medicine and Imaging and Cancer Research, having authored 10 papers that have together received 641 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (5 papers), AI in cancer detection (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Artificial Intelligence in Healthcare and Education (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), COVID-19 diagnosis using AI (2 papers), Network Security and Intrusion Detection (2 papers) and Cholangiocarcinoma and Gallbladder Cancer Studies (1 paper). The work is most often cited by research in Artificial Intelligence (518 citations), Health Informatics (17 citations), Computer Networks and Communications (193 citations), Signal Processing (83 citations) and Computer Vision and Pattern Recognition (93 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, Alexander Binder, Shoaib Ahmed Siddiqui, Emmanuel Müller, Grégoire Montavon, Klaus‐Robert Müller, Wojciech Samek and Yury Zemlyanskiy. Their work appears in journals such as Scientific Reports, European Journal of Cancer, Nature Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems 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.