Fernando Navarro

2.4k total citations
34 papers, 529 citations indexed

About

Fernando Navarro is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Fernando Navarro has authored 34 papers receiving a total of 529 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Pulmonary and Respiratory Medicine and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Fernando Navarro's work include Radiomics and Machine Learning in Medical Imaging (5 papers), Wound Healing and Treatments (3 papers) and Advanced Neural Network Applications (3 papers). Fernando Navarro is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (5 papers), Wound Healing and Treatments (3 papers) and Advanced Neural Network Applications (3 papers). Fernando Navarro collaborates with scholars based in United States, Germany and Switzerland. Fernando Navarro's co-authors include Dennis P. Orgill, Bjoern Menze, Marie L. Stoner, Fiona M. Wood, Giles Tetteh, Matthias Eiber, Hui Wang, Martin Krönke, Wolfgang Weber and Andrei Gafita and has published in prestigious journals such as Nature Communications, IEEE Transactions on Medical Imaging and Plastic & Reconstructive Surgery.

In The Last Decade

Fernando Navarro

27 papers receiving 512 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fernando Navarro United States 11 199 145 126 115 79 34 529
Ono I Japan 15 46 0.2× 151 1.0× 149 1.2× 49 0.4× 154 1.9× 66 705
Matthias Aust Germany 17 114 0.6× 74 0.5× 211 1.7× 43 0.4× 207 2.6× 34 961
Kwan Chul Tark South Korea 16 64 0.3× 43 0.3× 186 1.5× 28 0.2× 479 6.1× 44 841
Qing Lv China 14 39 0.2× 30 0.2× 32 0.3× 78 0.7× 191 2.4× 40 476
Harry M. Salinas United States 14 244 1.2× 72 0.5× 25 0.2× 208 1.8× 291 3.7× 21 618
Torsten Schulz Germany 11 68 0.3× 18 0.1× 57 0.5× 59 0.5× 112 1.4× 43 288
Weiyan Sun China 15 56 0.3× 185 1.3× 27 0.2× 156 1.4× 128 1.6× 43 551
Hisham Seify United States 10 21 0.1× 92 0.6× 67 0.5× 59 0.5× 398 5.0× 16 509
Masanobu Mizuta Japan 17 18 0.1× 227 1.6× 28 0.2× 51 0.4× 209 2.6× 65 710
Liu Ouyang China 10 70 0.4× 29 0.2× 48 0.4× 36 0.3× 41 0.5× 20 348

Countries citing papers authored by Fernando Navarro

Since Specialization
Citations

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

Fields of papers citing papers by Fernando Navarro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fernando Navarro

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

All Works

20 of 20 papers shown
1.
Peeken, Jan C., Stefan Münch, Lars Schüttrumpf, et al.. (2024). Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics analyses in extremity soft tissue sarcomas. Radiotherapy and Oncology. 197. 110338–110338. 5 indexed citations
2.
Navarro, Fernando, et al.. (2023). Focused Decoding Enables 3D Anatomical Detection by Transformers. Zurich Open Repository and Archive (University of Zurich). 2(February 2023). 72–95. 7 indexed citations
3.
Tetteh, Giles, Fernando Navarro, Raphael Meier, et al.. (2023). A deep learning approach to predict collateral flow in stroke patients using radiomic features from perfusion images. Frontiers in Neurology. 14. 1039693–1039693. 6 indexed citations
4.
Navarro, Fernando, Anjany Sekuboyina, Malek El Husseini, et al.. (2021). Automated detection of the contrast phase in MDCT by an artificial neural network improves the accuracy of opportunistic bone mineral density measurements. European Radiology. 32(3). 1465–1474. 23 indexed citations
5.
Kofler, Florian, Ivan Ezhov, Lucas Fidon, et al.. (2021). Robust, Primitive, and Unsupervised Quality Estimation for Segmentation Ensembles. Frontiers in Neuroscience. 15. 752780–752780. 2 indexed citations
6.
Navarro, Fernando, Hendrik Dapper, Carolin Knebel, et al.. (2021). Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging. Cancers. 13(12). 2866–2866. 40 indexed citations
7.
Schoppe, Oliver, Chenchen Pan, Hongcheng Mai, et al.. (2020). Deep learning-enabled multi-organ segmentation in whole-body mouse scans. Nature Communications. 11(1). 5626–5626. 72 indexed citations
8.
Gafita, Andrei, Martin Krönke, Giles Tetteh, et al.. (2019). qPSMA: Semiautomatic Software for Whole-Body Tumor Burden Assessment in Prostate Cancer Using 68Ga-PSMA11 PET/CT. Journal of Nuclear Medicine. 60(9). 1277–1283. 92 indexed citations
9.
Kocher, Madison, et al.. (2018). Colonic metastasis from infiltrating ductal breast carcinoma in a male patient: A case report. International Journal of Surgery Case Reports. 54. 34–38. 4 indexed citations
10.
Navarro, Fernando, et al.. (2017). Renal Myxoma, an Incidental Finding. Urology Case Reports. 13. 131–132. 3 indexed citations
11.
Bell, Richard M., et al.. (2015). Large prepatellar glomangioma: A case report. International Journal of Surgery Case Reports. 14. 80–84. 9 indexed citations
12.
Navarro, Fernando. (2008). Repertorio de siglas, acrónimos, abreviaturas y símbolos utilizados en los textos médicos en español. 9(27). 55–59.
13.
Montgomery, Diane & Fernando Navarro. (2008). Management of Constipation and Encopresis in Children. Journal of Pediatric Health Care. 22(3). 199–204. 13 indexed citations
14.
Lukens, Frank, et al.. (2006). Successful endoscopic resection of a gangliocytic paraganglioma of the minor papilla in a patient with pancreas divisum and pancreatitis (with video). Gastrointestinal Endoscopy. 65(3). 547–550. 14 indexed citations
15.
Navarro, Fernando, et al.. (2005). Panace@ — a successful open access journal from the STM translation community. Learned Publishing. 18(4). 258–269. 2 indexed citations
16.
Navarro, Fernando, et al.. (2004). Two-Photon Confocal Microscopy: A Nondestructive Method for Studying Wound Healing. Plastic & Reconstructive Surgery. 114(1). 121–128. 14 indexed citations
17.
Navarro, Fernando. (2002). Glosario de fármacos con nombre común no internacional (EN-ES). 3(7). 10–24. 1 indexed citations
18.
Butler, Charles E., Fernando Navarro, Christine Park, & Dennis P. Orgill. (2002). Regeneration of Neomucosa Using Cell-Seeded Collagen-GAG Matrices in Athymic Mice. Annals of Plastic Surgery. 48(3). 298–304. 5 indexed citations
19.
Navarro, Fernando, et al.. (2001). Perfusion of medium improves growth of human oral neomucosal tissue constructs. Wound Repair and Regeneration. 9(6). 507–512. 34 indexed citations
20.
Navarro, Fernando, et al.. (2001). Melanocyte Repopulation in Full-Thickness Wounds Using a Cell Spray Apparatus. Journal of Burn Care & Rehabilitation. 22(1). 41–46. 46 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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