Germán Corredor
Impact in
- Health Informatics top 5%
-
- Radiomics and Machine Learning in Medical Imaging
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 22
- Oncology 21
- Cancer Immunotherapy and Biomarkers 14
- Co-authors
- Anant Madabhushi (39 shared papers)Vamsidhar Velcheti (10 shared papers)Pingfu Fu (15 shared papers)Michael D. Feldman (3 shared papers)Prateek Prasanna (8 shared papers)Kaustav Bera (9 shared papers)Xiangxue Wang (6 shared papers)Cheng Lu (11 shared papers)
- Journals
- Journal of Clinical Oncology (7 papers)Oral Oncology (2 papers)Heliyon (2 papers)Journal for ImmunoTherapy of Cancer (2 papers)European Journal of Cancer (2 papers)
- Partner nations
- United StatesColombiaSwitzerland
In The Last Decade
Germán Corredor
52 papers receiving 964 citations
Peers
Comparison fields: 5 of 81
- Health Informatics 38
- Radiology, Nuclear Medicine and Imaging 441
- Oncology 262
- Artificial Intelligence 284
- Biophysics 50
Countries citing papers authored by Germán Corredor
This map shows the geographic impact of Germán Corredor'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 Germán Corredor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Germán Corredor more than expected).
Fields of papers citing papers by Germán Corredor
This network shows the impact of papers produced by Germán Corredor. 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 Germán Corredor. The network helps show where Germán Corredor may publish in the future.
Co-authors
The 25 scholars most cited alongside Germán Corredor, 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 56 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 208 | |
| 2 | 2018 | 170 | |
| 3 | 2020 | 100 | |
| 4 | 2022 | 92 | |
| 5 | 2018 | 77 | |
| 6 | 2022 | 60 | |
| 7 | 2020 | 44 | |
| 8 | 2022 | 33 | |
| 9 | 2025 | 19 | |
| 10 | 2023 | 17 | |
| 11 | 2022 | 15 | |
| 12 | 2018 | 14 | |
| 13 | 2022 | 13 | |
| 14 | 2022 | 12 | |
| 15 | 2024 | 10 | |
| 16 | 2015 | 10 | |
| 17 | 2023 | 7 | |
| 18 | 2024 | 5 | |
| 19 | 2018 | 5 | |
| 20 | 2022 | 5 |
About Germán Corredor
Germán Corredor is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology, Artificial Intelligence, Computer Vision and Pattern Recognition and Pulmonary and Respiratory Medicine, having authored 56 papers that have together received 971 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (22 papers), AI in cancer detection (21 papers), Cancer Immunotherapy and Biomarkers (14 papers), Ferroptosis and cancer prognosis (5 papers), Digital Imaging for Blood Diseases (5 papers), Image Retrieval and Classification Techniques (5 papers), Cell Image Analysis Techniques (4 papers) and Head and Neck Cancer Studies (3 papers). The work is most often cited by research in Health Informatics (38 citations), Radiology, Nuclear Medicine and Imaging (441 citations), Oncology (262 citations), Artificial Intelligence (284 citations) and Biophysics (50 citations). Germán Corredor has collaborated with scholars based in United States, Colombia and Switzerland. Frequent co-authors include Anant Madabhushi, Vamsidhar Velcheti, Pingfu Fu, Michael D. Feldman, Prateek Prasanna, Kaustav Bera, Xiangxue Wang, Cheng Lu, Priya Velu and David L. Rimm. Their work appears in journals such as Journal of Clinical Oncology, Oral Oncology, Heliyon, Journal for ImmunoTherapy of Cancer and European Journal of Cancer.
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.