Christoph Haarburger
- Radiology, Nuclear Medicine and Imaging top 5%
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Biomedical Engineering
- Health Informatics top 2%
- Co-authors
- Daniel TruhnChristiane KühlDorit MerhofGustav Müller‐FranzesSimone SchradingHannah SchneiderSven NebelungTianyu Han
- Topics
- Radiomics and Machine Learning in Medical Imaging (8 papers)Generative Adversarial Networks and Image Synthesis (4 papers)Artificial Intelligence in Healthcare and Education (4 papers)
- Partner nations
- GermanyUnited KingdomSwitzerland
In The Last Decade
Christoph Haarburger
16 papers receiving 666 citations
Hit Papers
Peers
Comparison fields: 5 of 87
- Radiology, Nuclear Medicine and Imaging 446
- Artificial Intelligence 310
- Computer Vision and Pattern Recognition 105
- Biomedical Engineering 93
- Health Informatics 80
Countries citing papers authored by Christoph Haarburger
This map shows the geographic impact of Christoph Haarburger'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 Christoph Haarburger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christoph Haarburger more than expected).
Fields of papers citing papers by Christoph Haarburger
This network shows the impact of papers produced by Christoph Haarburger. 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 Christoph Haarburger. The network helps show where Christoph Haarburger may publish in the future.
Co-authorship network of co-authors of Christoph Haarburger
This figure shows the co-authorship network connecting the top 25 collaborators of Christoph Haarburger. A scholar is included among the top collaborators of Christoph Haarburger 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 Christoph Haarburger. Christoph Haarburger is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 17 | |
| 2 | Denoising diffusion probabilistic models for 3D medical image generationbreakdown → | 121 |
| 3 | 39 | |
| 4 | 21 | |
| 5 | 79 | |
| 6 | 3 | |
| 7 | 14 | |
| 8 | 4 | |
| 9 | 24 | |
| 10 | 92 | |
| 11 | 10 | |
| 12 | 35 | |
| 13 | 17 | |
| 14 | 7 | |
| 15 | 191 | |
| 16 | 2 |
About Christoph Haarburger
Christoph Haarburger is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 16 papers that have together received 676 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (8 papers), Generative Adversarial Networks and Image Synthesis (4 papers) and Artificial Intelligence in Healthcare and Education (4 papers). The work is most often cited by research in Health Informatics (80 citations), Radiology, Nuclear Medicine and Imaging (446 citations) and Artificial Intelligence (310 citations). Christoph Haarburger has collaborated with scholars based in Germany, United Kingdom and Switzerland. Frequent co-authors include Daniel Truhn, Christiane Kühl, Dorit Merhof, Gustav Müller‐Franzes, Simone Schrading, Hannah Schneider, Sven Nebelung, Tianyu Han, Jakob Nikolas Kather and Firas Khader. Their work appears in journals such as Nature Communications, Scientific Reports and Radiology.
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.