Gustav Müller‐Franzes
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education 5
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- Radiomics and Machine Learning in Medical Imaging 16
- MRI in cancer diagnosis 5
- COVID-19 diagnosis using AI 3
- Artificial Intelligence top 5%
- AI in cancer detection 7
- Machine Learning in Healthcare 2
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- Generative Adversarial Networks and Image Synthesis 3
- Health Information Management top 10%
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- Advanced X-ray and CT Imaging 4
- Co-authors
- Daniel TruhnChristiane KühlJakob Nikolas KatherChristoph HaarburgerSven NebelungFiras KhaderTianyu HanSoroosh Tayebi Arasteh
- Partner nations
- GermanyUnited KingdomNetherlands
In The Last Decade
Gustav Müller‐Franzes
22 papers receiving 559 citations
Hit Papers
Peers
Comparison fields: 5 of 84
- Health Informatics 99
- Radiology, Nuclear Medicine and Imaging 316
- Artificial Intelligence 239
- Computer Vision and Pattern Recognition 89
- Health Information Management 19
Countries citing papers authored by Gustav Müller‐Franzes
This map shows the geographic impact of Gustav Müller‐Franzes'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 Gustav Müller‐Franzes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gustav Müller‐Franzes more than expected).
Fields of papers citing papers by Gustav Müller‐Franzes
This network shows the impact of papers produced by Gustav Müller‐Franzes. 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 Gustav Müller‐Franzes. The network helps show where Gustav Müller‐Franzes may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Gustav Müller‐Franzes, 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 | 3 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 11 | |
| 4 | 2024 | 17 | |
| 5 | 2024 | 5 | |
| 6 | In-context learning enables multimodal large language models to classify cancer pathology imagesbreakdown → | 2024 | 47 |
| 7 | 2024 | 5 | |
| 8 | 2024 | 3 | |
| 9 | 2024 | 2 | |
| 10 | Denoising diffusion probabilistic models for 3D medical image generationbreakdown → | 2023 | 121 |
| 11 | 2023 | 8 | |
| 12 | 2023 | 2 | |
| 13 | 2023 | 39 | |
| 14 | 2023 | 21 | |
| 15 | 2023 | 55 | |
| 16 | 2023 | 79 | |
| 17 | 2023 | 31 | |
| 18 | 2022 | 6 | |
| 19 | 2021 | 3 | |
| 20 | 2020 | 92 |
About Gustav Müller‐Franzes
Gustav Müller‐Franzes is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Equine, having authored 22 papers that have together received 567 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (16 papers), AI in cancer detection (7 papers), MRI in cancer diagnosis (5 papers), Artificial Intelligence in Healthcare and Education (5 papers), Advanced X-ray and CT Imaging (4 papers), Generative Adversarial Networks and Image Synthesis (3 papers), COVID-19 diagnosis using AI (3 papers) and Machine Learning in Healthcare (2 papers). The work is most often cited by research in Health Informatics (99 citations), Radiology, Nuclear Medicine and Imaging (316 citations) and Artificial Intelligence (239 citations). Gustav Müller‐Franzes has collaborated with scholars based in Germany, United Kingdom and Netherlands. Frequent co-authors include Daniel Truhn, Christiane Kühl, Jakob Nikolas Kather, Christoph Haarburger, Sven Nebelung, Firas Khader, Tianyu Han, Soroosh Tayebi Arasteh, Sebastian Foersch and Dorit Merhof. 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.