Lukas Ruff

1.7k total citations · 1 hit paper
10 papers, 641 citations indexed

About

Lukas Ruff is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Networks and Communications. According to data from OpenAlex, Lukas Ruff has authored 10 papers receiving a total of 641 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 4 papers in Radiology, Nuclear Medicine and Imaging and 2 papers in Computer Networks and Communications. Recurrent topics in Lukas Ruff's work include Anomaly Detection Techniques and Applications (5 papers), AI in cancer detection (3 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Lukas Ruff is often cited by papers focused on Anomaly Detection Techniques and Applications (5 papers), AI in cancer detection (3 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Lukas Ruff collaborates with scholars based in Germany, South Korea and United Kingdom. Lukas Ruff's co-authors include Robert A. Vandermeulen, Marius Kloft, Lucas Deecke, Emmanuel Müller, Shoaib Ahmed Siddiqui, Alexander Binder, Klaus‐Robert Müller, Grégoire Montavon, Wojciech Samek and Yury Zemlyanskiy and has published in prestigious journals such as Scientific Reports, European Journal of Cancer and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Lukas Ruff

10 papers receiving 619 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep One-Class Classification

2018 484 citations Lukas Ruff, Robert A. Vandermeulen et al. International Conference on Machine Learning profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Lukas Ruff Germany	5	518	193	93	83	72	10	641
Lucas Deecke United Kingdom	2	422 0.8×	174 0.9×	76 0.8×	71 0.9×	64 0.9×	2	488
Latha Pemula Germany	1	469 0.9×	134 0.7×	155 1.7×	43 0.5×	43 0.6×	2	622
Joaquin Zepeda France	5	488 0.9×	134 0.7×	203 2.2×	48 0.6×	43 0.6×	9	676
Dae-Ki Cho United States	8	565 1.1×	487 2.5×	124 1.3×	199 2.4×	47 0.7×	11	886
Barath Narayanan Narayanan United States	16	289 0.6×	167 0.9×	171 1.8×	184 2.2×	11 0.2×	33	746
Fen Wang China	13	361 0.7×	257 1.3×	63 0.7×	87 1.0×	31 0.4×	59	615
Xia Chen China	16	294 0.6×	153 0.8×	398 4.3×	41 0.5×	20 0.3×	59	753
Priyadip Ray India	13	223 0.4×	131 0.7×	47 0.5×	25 0.3×	19 0.3×	55	815
Moin Nabi Italy	11	457 0.9×	108 0.6×	379 4.1×	40 0.5×	22 0.3×	26	598
Xin Ding China	11	218 0.4×	53 0.3×	178 1.9×	130 1.6×	13 0.2×	56	585

Countries citing papers authored by Lukas Ruff

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Lukas Ruff

This figure shows the co-authorship network connecting the top 25 collaborators of Lukas Ruff. A scholar is included among the top collaborators of Lukas Ruff based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lukas Ruff. Lukas Ruff is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Ruff, Lukas, et al.. (2025). Explainable AI reveals Clever Hans effects in unsupervised learning models. Nature Machine Intelligence. 7(3). 412–422. 2 indexed citations

2.

Schallenberg, Simon, Marius Kloft, David Horst, et al.. (2024). AI-Based Anomaly Detection for Clinical-Grade Histopathological Diagnostics. NEJM AI. 1(11). 4 indexed citations

3.

Ruff, Lukas, Maximilian Alber, Simon Schallenberg, et al.. (2024). Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study. European Journal of Cancer. 211. 114292–114292. 2 indexed citations

4.

Ruff, Lukas, Maximilian Alber, Simon Schallenberg, et al.. (2024). Leveraging weak complementary labels enhances semantic segmentation of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Scientific Reports. 14(1). 24988–24988. 1 indexed citations

5.

Klauschen, Frederick, Philipp Jurmeister, Michael Bockmayr, et al.. (2023). Toward Explainable Artificial Intelligence for Precision Pathology. Annual Review of Pathology Mechanisms of Disease. 19(1). 541–570. 57 indexed citations

6.

Ruff, Lukas, et al.. (2022). From Clustering to Cluster Explanations via Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 35(2). 1926–1940. 44 indexed citations

7.

Ruff, Lukas, et al.. (2021). Explainable Deep One-Class Classification. arXiv (Cornell University). 9 indexed citations

8.

Deecke, Lucas, Lukas Ruff, Robert A. Vandermeulen, & Hakan Bilen. (2021). Transfer-Based Semantic Anomaly Detection. Edinburgh Research Explorer (University of Edinburgh). 2546–2558. 4 indexed citations

9.

Ruff, Lukas, et al.. (2019). Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text. 4061–4071. 34 indexed citations

10.

Ruff, Lukas, Robert A. Vandermeulen, Lucas Deecke, et al.. (2018). Deep One-Class Classification. International Conference on Machine Learning. 4393–4402. 484 indexed citations breakdown →

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.

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