Marc Fischer
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- Radiomics and Machine Learning in Medical Imaging 6
- COVID-19 diagnosis using AI 2
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- Adversarial Robustness in Machine Learning 3
- Domain Adaptation and Few-Shot Learning 2
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- Advanced Neural Network Applications 3
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- Advanced X-ray and CT Imaging 2
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- Orthopaedic implants and arthroplasty 1
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- Aortic Disease and Treatment Approaches 1
- Co-authors
- Sergios GatidisTobias HeppBin YangFabian BambergMartin VechevThomas KüstnerKonstantin NikolaouPhilipp Sommer
- Journals
- Radiology Artificial Intelligence (2 papers)Investigative Radiology (1 paper)Cannabis and Cannabinoid Research (1 paper)
- Partner nations
- GermanySwitzerlandUnited Kingdom
In The Last Decade
Marc Fischer
21 papers receiving 229 citations
Peers
Comparison fields: 5 of 77
- Health Informatics 5
- Metals and Alloys 8
- Radiology, Nuclear Medicine and Imaging 67
- Artificial Intelligence 66
- Computer Vision and Pattern Recognition 35
Countries citing papers authored by Marc Fischer
This map shows the geographic impact of Marc Fischer'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 Marc Fischer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marc Fischer more than expected).
Fields of papers citing papers by Marc Fischer
This network shows the impact of papers produced by Marc Fischer. 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 Marc Fischer. The network helps show where Marc Fischer may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Marc Fischer, 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 | 2024 | 0 | |
| 2 | 2023 | 15 | |
| 3 | 2023 | 9 | |
| 4 | 2023 | 2 | |
| 5 | 2022 | 13 | |
| 6 | 2022 | 10 | |
| 7 | 2021 | 5 | |
| 8 | 2021 | 4 | |
| 9 | 2021 | 9 | |
| 10 | 2021 | 1 | |
| 11 | 2021 | 32 | |
| 12 | 2020 | 15 | |
| 13 | 2020 | 30 | |
| 14 | DL2: Training and Querying Neural Networks with Logic. | 2019 | 26 |
| 15 | Distilled Agent DQN for Provable Adversarial Robustness | 2018 | 3 |
| 16 | 2018 | 10 | |
| 17 | 1977 | 2 | |
| 18 | 1975 | 13 | |
| 19 | 1974 | 6 | |
| 20 | 1971 | 2 |
About Marc Fischer
Marc Fischer is a scholar working on Hardware and Architecture, Radiology, Nuclear Medicine and Imaging and Industrial and Manufacturing Engineering, having authored 23 papers that have together received 233 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (6 papers), Adversarial Robustness in Machine Learning (3 papers), Advanced Neural Network Applications (3 papers), Advanced X-ray and CT Imaging (2 papers), COVID-19 diagnosis using AI (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Orthopaedic implants and arthroplasty (1 paper) and Aortic Disease and Treatment Approaches (1 paper). The work is most often cited by research in Health Informatics (5 citations), Metals and Alloys (8 citations) and Radiology, Nuclear Medicine and Imaging (67 citations). Marc Fischer has collaborated with scholars based in Germany, Switzerland and United Kingdom. Frequent co-authors include Sergios Gatidis, Tobias Hepp, Bin Yang, Fabian Bamberg, Martin Vechev, Thomas Küstner, Konstantin Nikolaou, Philipp Sommer, Jan Schlechtendahl and Mislav Balunović. Their work appears in journals such as Radiology Artificial Intelligence, Investigative Radiology, Cannabis and Cannabinoid Research, Computerized Medical Imaging and Graphics and Corrosion Science.
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.