Ivan Coronado

12 papers receiving 323 citations

Peers

Ivan Coronado
Comparison fields: 5 of 54
  • Neurology 98
  • Health Informatics 9
  • Radiology, Nuclear Medicine and Imaging 145
  • Pathology and Forensic Medicine 101
  • Computer Vision and Pattern Recognition 84
Replace Sheeba J. Sujit with:
Sheeba J. Sujit United States
Jacob C. Reinhold United States
Rasoul Khayati Iran
Seyed Raein Hashemi United States
Shahab Aslani United Kingdom
Max W. K. Law Hong Kong
Simon Francis Canada
Jiancong Wang United States
Lucas Fidon United Kingdom
A. Saura Quiles Spain
Ivan Coronado relative to Sheeba J. Sujit United States Sheeba J. Sujit's profile →
Citations per field
00.5×1.5×
Sheeba J. Sujit · 1×
Citations per year

Countries citing papers authored by Ivan Coronado

Since Specialization
Citations

This map shows the geographic impact of Ivan Coronado'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 Ivan Coronado with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivan Coronado more than expected).

Fields of papers citing papers by Ivan Coronado

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ivan Coronado. 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 Ivan Coronado. The network helps show where Ivan Coronado may publish in the future.

Co-authors

The 19 scholars most cited alongside Ivan Coronado, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ivan Coronado Line = papers co-authored together Ivan Coronado links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 201980
2 201961
3 201949
4 201939
5 202036
6 201924
7 202214
8 20188
9 20186
10 20215
11 20215
12 20233

About Ivan Coronado

Ivan Coronado is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Pathology and Forensic Medicine, Computer Vision and Pattern Recognition and Biomedical Engineering, having authored 12 papers that have together received 330 indexed citations. Recurring topics across this work include Ultrasound Imaging and Elastography (5 papers), AI in cancer detection (5 papers), Multiple Sclerosis Research Studies (4 papers), Retinal Imaging and Analysis (3 papers), Medical Image Segmentation Techniques (3 papers), Image Processing Techniques and Applications (2 papers), Brain Tumor Detection and Classification (2 papers) and Ultrasound and Hyperthermia Applications (2 papers). The work is most often cited by research in Neurology (98 citations), Health Informatics (9 citations), Radiology, Nuclear Medicine and Imaging (145 citations), Pathology and Forensic Medicine (101 citations) and Computer Vision and Pattern Recognition (84 citations). Ivan Coronado has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Ponnada A. Narayana, Refaat E. Gabr, Sheeba J. Sujit, Jerry S. Wolinsky, Fred Lublin, Xiaojun Sun, Arash Kamali, Melvin Robinson, Sushmita Datta and Luca Giancardo. Their work appears in journals such as Multiple Sclerosis Journal, Journal of Magnetic Resonance Imaging, Scientific Reports, Radiology and Journal of Clinical Medicine.

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.

Explore authors with similar magnitude of impact