Tianyu Han
- Radiology, Nuclear Medicine and Imaging top 5%
- Artificial Intelligence top 5%
- Health Informatics top 1%
- Computer Vision and Pattern Recognition top 10%
- Biomedical Engineering
- Co-authors
- Daniel TruhnSven NebelungJakob Nikolas KatherChristiane KühlSoroosh Tayebi ArastehChristoph HaarburgerGustav Müller‐FranzesFiras Khader
- Topics
- Radiomics and Machine Learning in Medical Imaging (10 papers)Artificial Intelligence in Healthcare and Education (9 papers)Machine Learning in Healthcare (6 papers)
- Partner nations
- GermanyUnited KingdomAustria
In The Last Decade
Tianyu Han
18 papers receiving 541 citations
Hit Papers
Peers
Comparison fields: 5 of 98
- Radiology, Nuclear Medicine and Imaging 245
- Artificial Intelligence 243
- Health Informatics 132
- Computer Vision and Pattern Recognition 101
- Biomedical Engineering 43
Countries citing papers authored by Tianyu Han
This map shows the geographic impact of Tianyu Han'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 Tianyu Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tianyu Han more than expected).
Fields of papers citing papers by Tianyu Han
This network shows the impact of papers produced by Tianyu Han. 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 Tianyu Han. The network helps show where Tianyu Han may publish in the future.
Co-authorship network of co-authors of Tianyu Han
This figure shows the co-authorship network connecting the top 25 collaborators of Tianyu Han. A scholar is included among the top collaborators of Tianyu Han 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 Tianyu Han. Tianyu Han is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 9 | |
| 5 | 17 | |
| 6 | 2 | |
| 7 | 5 | |
| 8 | 43 | |
| 9 | 55 | |
| 10 | 2 | |
| 11 | Denoising diffusion probabilistic models for 3D medical image generationbreakdown → | 121 |
| 12 | 39 | |
| 13 | 21 | |
| 14 | 79 | |
| 15 | 31 | |
| 16 | 53 | |
| 17 | 14 | |
| 18 | 24 | |
| 19 | 35 |
About Tianyu Han
Tianyu Han is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Family Practice, having authored 19 papers that have together received 552 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (10 papers), Artificial Intelligence in Healthcare and Education (9 papers) and Machine Learning in Healthcare (6 papers). The work is most often cited by research in Health Informatics (132 citations), Radiology, Nuclear Medicine and Imaging (245 citations) and Artificial Intelligence (243 citations). Tianyu Han has collaborated with scholars based in Germany, United Kingdom and Austria. Frequent co-authors include Daniel Truhn, Sven Nebelung, Jakob Nikolas Kather, Christiane Kühl, Soroosh Tayebi Arasteh, Christoph Haarburger, Gustav Müller‐Franzes, Firas Khader, Keno K. Bressem and Volkmar Schulz. Their work appears in journals such as JAMA, Nature Communications and Biomaterials.
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.