Ivan Coronado

483 total citations
12 papers, 330 citations indexed

About

Ivan Coronado is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pathology and Forensic Medicine. According to data from OpenAlex, Ivan Coronado has authored 12 papers receiving a total of 330 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Artificial Intelligence and 4 papers in Pathology and Forensic Medicine. Recurrent topics in Ivan Coronado's work include Ultrasound Imaging and Elastography (5 papers), AI in cancer detection (5 papers) and Multiple Sclerosis Research Studies (4 papers). Ivan Coronado is often cited by papers focused on Ultrasound Imaging and Elastography (5 papers), AI in cancer detection (5 papers) and Multiple Sclerosis Research Studies (4 papers). Ivan Coronado collaborates with scholars based in United States and United Kingdom. Ivan Coronado's co-authors include Refaat E. Gabr, Ponnada A. Narayana, Sheeba J. Sujit, Jerry S. Wolinsky, Fred Lublin, Xiaojun Sun, Arash Kamali, Sushmita Datta, Melvin Robinson and Luca Giancardo and has published in prestigious journals such as Scientific Reports, Radiology and Journal of Magnetic Resonance Imaging.

In The Last Decade

Ivan Coronado

12 papers receiving 323 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ivan Coronado United States	8	145	101	98	84	58	12	330
Rasoul Khayati Iran	8	103 0.7×	75 0.7×	148 1.5×	175 2.1×	21 0.4×	22	373
Simon Francis Canada	4	149 1.0×	102 1.0×	128 1.3×	191 2.3×	44 0.8×	8	398
Jacob C. Reinhold United States	4	210 1.4×	28 0.3×	46 0.5×	105 1.3×	62 1.1×	7	393
Sheeba J. Sujit United States	7	117 0.8×	83 0.8×	83 0.8×	71 0.8×	48 0.8×	10	304
A. Saura Quiles Spain	5	58 0.4×	73 0.7×	68 0.7×	105 1.3×	28 0.5×	10	243
Shahab Aslani United Kingdom	6	150 1.0×	31 0.3×	55 0.6×	127 1.5×	21 0.4×	8	248
Liyu Wei China	2	118 0.8×	130 1.3×	45 0.5×	133 1.6×	28 0.5×	5	316
Seyed Raein Hashemi United States	6	90 0.6×	21 0.2×	43 0.4×	99 1.2×	30 0.5×	7	242
S. Prima France	6	86 0.6×	47 0.5×	36 0.4×	175 2.1×	39 0.7×	7	303
Max W. K. Law Hong Kong	10	121 0.8×	39 0.4×	15 0.2×	162 1.9×	96 1.7×	17	310

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-authorship network of co-authors of Ivan Coronado

This figure shows the co-authorship network connecting the top 25 collaborators of Ivan Coronado. A scholar is included among the top collaborators of Ivan Coronado 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 Ivan Coronado. Ivan Coronado is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

Coronado, Ivan, Samiksha Pachade, Emanuele Trucco, et al.. (2023). Synthetic OCT-A blood vessel maps using fundus images and generative adversarial networks. Scientific Reports. 13(1). 15325–15325. 3 indexed citations

Pachade, Samiksha, Ivan Coronado, Rania Abdelkhaleq, et al.. (2022). Detection of Stroke with Retinal Microvascular Density and Self-Supervised Learning Using OCT-A and Fundus Imaging. Journal of Clinical Medicine. 11(24). 7408–7408. 14 indexed citations

Sujit, Sheeba J., et al.. (2021). Deep learning enabled brain shunt valve identification using mobile phones. Computer Methods and Programs in Biomedicine. 210. 106356–106356. 5 indexed citations

Coronado, Ivan, Rania Abdelkhaleq, Roomasa Channa, et al.. (2021). Towards Stroke Biomarkers on Fundus Retinal Imaging: A Comparison Between Vasculature Embeddings and General Purpose Convolutional Neural Networks. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2021. 3873–3876. 5 indexed citations

Coronado, Ivan, Refaat E. Gabr, & Ponnada A. Narayana. (2020). Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis. Multiple Sclerosis Journal. 27(4). 519–527. 36 indexed citations

Narayana, Ponnada A., Ivan Coronado, Sheeba J. Sujit, et al.. (2019). Deep Learning for Predicting Enhancing Lesions in Multiple Sclerosis from Noncontrast MRI. Radiology. 294(2). 398–404. 80 indexed citations

Narayana, Ponnada A., Ivan Coronado, Sheeba J. Sujit, et al.. (2019). Are multi-contrast magnetic resonance images necessary for segmenting multiple sclerosis brains? A large cohort study based on deep learning. Magnetic Resonance Imaging. 65. 8–14. 24 indexed citations

Sujit, Sheeba J., Ivan Coronado, Arash Kamali, Ponnada A. Narayana, & Refaat E. Gabr. (2019). Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks. Journal of Magnetic Resonance Imaging. 50(4). 1260–1267. 49 indexed citations

Narayana, Ponnada A., Ivan Coronado, Sheeba J. Sujit, et al.. (2019). Deep‐Learning‐Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size. Journal of Magnetic Resonance Imaging. 51(5). 1487–1496. 39 indexed citations

10.

Gabr, Refaat E., Ivan Coronado, Melvin Robinson, et al.. (2019). Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study. Multiple Sclerosis Journal. 26(10). 1217–1226. 61 indexed citations

11.

Sujit, Sheeba J., Refaat E. Gabr, Ivan Coronado, et al.. (2018). Automated Image Quality Evaluation of Structural Brain Magnetic Resonance Images using Deep Convolutional Neural Networks. 33–36. 8 indexed citations

12.

Narayana, Ponnada A., Ivan Coronado, Melvin Robinson, et al.. (2018). Multimodal MRI Segmentation of Brain Tissue and T2-Hyperintense White Matter Lesions in Multiple Sclerosis using Deep Convolutional Neural Networks and a Large Multi-center Image Database. 13–16. 6 indexed citations

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.

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