Roman Zeleznik
- Radiology, Nuclear Medicine and Imaging top 2%
- Pulmonary and Respiratory Medicine top 5%
- Artificial Intelligence top 5%
- Biomedical Engineering
- Oncology
- Co-authors
- Hugo J.W.L. AertsRaymond H. MakChintan ParmarAhmed HosnyThibaud CorollerIdalid FrancoYiwen XuRobert J. Gillies
- Topics
- Radiomics and Machine Learning in Medical Imaging (7 papers)Cardiac Imaging and Diagnostics (7 papers)Cardiovascular Disease and Adiposity (4 papers)
- Cited by
- Health InformaticsRadiology, Nuclear Medicine and ImagingPulmonary and Respiratory Medicine
- Journals
- SHILAP Revista de lepidopterologíaRadiologyClinical Cancer Research
- Partner nations
- United StatesNetherlandsGermany
In The Last Decade
Roman Zeleznik
12 papers receiving 877 citations
Hit Papers
Peers
Comparison fields: 5 of 84
- Radiology, Nuclear Medicine and Imaging 716
- Pulmonary and Respiratory Medicine 399
- Artificial Intelligence 271
- Biomedical Engineering 184
- Oncology 82
Countries citing papers authored by Roman Zeleznik
This map shows the geographic impact of Roman Zeleznik'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 Roman Zeleznik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roman Zeleznik more than expected).
Fields of papers citing papers by Roman Zeleznik
This network shows the impact of papers produced by Roman Zeleznik. 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 Roman Zeleznik. The network helps show where Roman Zeleznik may publish in the future.
Co-authorship network of co-authors of Roman Zeleznik
This figure shows the co-authorship network connecting the top 25 collaborators of Roman Zeleznik. A scholar is included among the top collaborators of Roman Zeleznik 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 Roman Zeleznik. Roman Zeleznik is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 9 | |
| 3 | 6 | |
| 4 | 0 | |
| 5 | 10 | |
| 6 | 3 | |
| 7 | 16 | |
| 8 | 0 | |
| 9 | 25 | |
| 10 | 11 | |
| 11 | 8 | |
| 12 | Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imagingbreakdown → | 402 |
| 13 | 0 | |
| 14 | Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics studybreakdown → | 398 |
| 15 | 1 |
About Roman Zeleznik
Roman Zeleznik is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Radiation, having authored 15 papers that have together received 892 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (7 papers), Cardiac Imaging and Diagnostics (7 papers) and Cardiovascular Disease and Adiposity (4 papers). The work is most often cited by research in Health Informatics (77 citations), Radiology, Nuclear Medicine and Imaging (716 citations) and Pulmonary and Respiratory Medicine (399 citations). Roman Zeleznik has collaborated with scholars based in United States, Netherlands and Germany. Frequent co-authors include Hugo J.W.L. Aerts, Raymond H. Mak, Chintan Parmar, Ahmed Hosny, Thibaud Coroller, Idalid Franco, Yiwen Xu, Robert J. Gillies, Johan Bussink and Patrick Großmann. Their work appears in journals such as SHILAP Revista de lepidopterología, Radiology and Clinical Cancer Research.
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.