Gilmer Valdés
- Radiology, Nuclear Medicine and Imaging top 1%
- Radiation top 0.5%
- Biomedical Engineering top 5%
- Pulmonary and Respiratory Medicine top 5%
- Artificial Intelligence top 5%
- Co-authors
- Timothy D. SolbergOlivier MorinCharles B. SimoneVasant KearneyRyan ScheuermannMaria F. ChanLyle UngarSue S. Yom
- Topics
- Advanced Radiotherapy Techniques (30 papers)Radiomics and Machine Learning in Medical Imaging (21 papers)Lung Cancer Diagnosis and Treatment (12 papers)
- Journals
- Proceedings of the National Academy of SciencesPLoS ONEIEEE Transactions on Pattern Analysis and Machine Intelligence
- Partner nations
- United StatesCanadaNetherlands
In The Last Decade
Gilmer Valdés
56 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 145
- Radiology, Nuclear Medicine and Imaging 1.3k
- Radiation 880
- Biomedical Engineering 604
- Pulmonary and Respiratory Medicine 490
- Artificial Intelligence 348
Countries citing papers authored by Gilmer Valdés
This map shows the geographic impact of Gilmer Valdés'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 Gilmer Valdés with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gilmer Valdés more than expected).
Fields of papers citing papers by Gilmer Valdés
This network shows the impact of papers produced by Gilmer Valdés. 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 Gilmer Valdés. The network helps show where Gilmer Valdés may publish in the future.
Co-authorship network of co-authors of Gilmer Valdés
This figure shows the co-authorship network connecting the top 25 collaborators of Gilmer Valdés. A scholar is included among the top collaborators of Gilmer Valdés 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 Gilmer Valdés. Gilmer Valdés 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 | 10 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 3 | |
| 7 | 2 | |
| 8 | 48 | |
| 9 | 7 | |
| 10 | 24 | |
| 11 | Artificial intelligence and machine learning for medical imaging: A technology reviewbreakdown → | 236 |
| 12 | 10 | |
| 13 | 12 | |
| 14 | 50 | |
| 15 | Training Deep Learning models with small datasets. | 7 |
| 16 | 69 | |
| 17 | 75 | |
| 18 | 78 | |
| 19 | 30 | |
| 20 | 7 |
About Gilmer Valdés
Gilmer Valdés is a scholar working on Radiation, Health Informatics and Radiology, Nuclear Medicine and Imaging, having authored 59 papers that have together received 2.0k indexed citations. Recurring topics across this work include Advanced Radiotherapy Techniques (30 papers), Radiomics and Machine Learning in Medical Imaging (21 papers) and Lung Cancer Diagnosis and Treatment (12 papers). The work is most often cited by research in Health Informatics (306 citations), Radiation (880 citations) and Radiology, Nuclear Medicine and Imaging (1.3k citations). Gilmer Valdés has collaborated with scholars based in United States, Canada and Netherlands. Frequent co-authors include Timothy D. Solberg, Olivier Morin, Charles B. Simone, Vasant Kearney, Ryan Scheuermann, Maria F. Chan, Lyle Ungar, Sue S. Yom, Alon Witztum and José Marcio Luna. Their work appears in journals such as Proceedings of the National Academy of Sciences, PLoS ONE and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.