Sanja Antic
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
- COVID-19 diagnosis using AI
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
Papers in
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- Lung Cancer Diagnosis and Treatment 21
- Lung Cancer Treatments and Mutations 6
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- Radiomics and Machine Learning in Medical Imaging 18
- Medical Imaging Techniques and Applications 6
- COVID-19 diagnosis using AI 5
- Co-authors
- Pierre P. Massion (21 shared papers)Bennett A. Landman (17 shared papers)Gary T. Smith (3 shared papers)Ronald C. Walker (6 shared papers)Riqiang Gao (11 shared papers)Kim L. Sandler (13 shared papers)Yuankai Huo (9 shared papers)Matthew B. Schabath (2 shared papers)
- Journals
- Cancer Biomarkers (2 papers)Scientific Reports (2 papers)BMC Cancer (2 papers)Medical Image Analysis (2 papers)PLoS ONE (2 papers)
- Partner nations
- United StatesChinaCzechia
In The Last Decade
Sanja Antic
26 papers receiving 381 citations
Peers
Comparison fields: 5 of 56
- Radiology, Nuclear Medicine and Imaging 296
- Health Informatics 17
- Pulmonary and Respiratory Medicine 270
- Artificial Intelligence 56
- Cancer Research 21
Countries citing papers authored by Sanja Antic
This map shows the geographic impact of Sanja Antic'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 Sanja Antic with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sanja Antic more than expected).
Fields of papers citing papers by Sanja Antic
This network shows the impact of papers produced by Sanja Antic. 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 Sanja Antic. The network helps show where Sanja Antic may publish in the future.
Co-authors
The 25 scholars most cited alongside Sanja Antic, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 109 | |
| 2 | 2016 | 80 | |
| 3 | 2020 | 28 | |
| 4 | 2020 | 22 | |
| 5 | 2018 | 17 | |
| 6 | 2019 | 15 | |
| 7 | 2022 | 12 | |
| 8 | 2023 | 11 | |
| 9 | 2023 | 11 | |
| 10 | 2020 | 10 | |
| 11 | 2018 | 10 | |
| 12 | 2022 | 10 | |
| 13 | 2023 | 8 | |
| 14 | 2022 | 6 | |
| 15 | 2021 | 6 | |
| 16 | 2023 | 6 | |
| 17 | 2020 | 6 | |
| 18 | 2020 | 5 | |
| 19 | 2023 | 4 | |
| 20 | 2023 | 3 |
About Sanja Antic
Sanja Antic is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Oncology, Immunology and Surgery, having authored 28 papers that have together received 385 indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (21 papers), Radiomics and Machine Learning in Medical Imaging (18 papers), Medical Imaging Techniques and Applications (6 papers), Lung Cancer Treatments and Mutations (6 papers), COVID-19 diagnosis using AI (5 papers), Mast cells and histamine (1 paper), Body Composition Measurement Techniques (1 paper) and TGF-β signaling in diseases (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (296 citations), Health Informatics (17 citations), Pulmonary and Respiratory Medicine (270 citations), Artificial Intelligence (56 citations) and Cancer Research (21 citations). Sanja Antic has collaborated with scholars based in United States, China and Czechia. Frequent co-authors include Pierre P. Massion, Bennett A. Landman, Gary T. Smith, Ronald C. Walker, Riqiang Gao, Kim L. Sandler, Yuankai Huo, Matthew B. Schabath, Ying Liu and Yoganand Balagurunathan. Their work appears in journals such as Cancer Biomarkers, Scientific Reports, BMC Cancer, Medical Image Analysis and PLoS ONE.
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.