Jorge Novo

2.8k total citations
142 papers, 1.7k citations indexed

About

Jorge Novo is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jorge Novo has authored 142 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 108 papers in Radiology, Nuclear Medicine and Imaging, 69 papers in Ophthalmology and 30 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jorge Novo's work include Retinal Imaging and Analysis (81 papers), Retinal Diseases and Treatments (48 papers) and Glaucoma and retinal disorders (36 papers). Jorge Novo is often cited by papers focused on Retinal Imaging and Analysis (81 papers), Retinal Diseases and Treatments (48 papers) and Glaucoma and retinal disorders (36 papers). Jorge Novo collaborates with scholars based in Spain, Portugal and United States. Jorge Novo's co-authors include Marcos Ortega, José Rouco, Joaquim de Moura, Manuel G. Penedo, Álvaro S. Hervella, Aurélio Campilho, L.M. Gonçalves, N. Barreira, Lucía Ramos and José Sántos and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Jorge Novo

132 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jorge Novo Spain 22 1.1k 605 341 293 293 142 1.7k
José Rouco Spain 18 899 0.8× 312 0.5× 524 1.5× 651 2.2× 148 0.5× 49 1.5k
Manuel G. Penedo Spain 23 1.0k 1.0× 751 1.2× 594 1.7× 273 0.9× 204 0.7× 121 1.9k
Jeny Rajan India 26 921 0.9× 273 0.5× 884 2.6× 257 0.9× 260 0.9× 84 1.9k
Dwarikanath Mahapatra Switzerland 23 905 0.9× 352 0.6× 1.1k 3.1× 464 1.6× 207 0.7× 87 1.8k
Abhijit Guha Roy Germany 13 776 0.7× 294 0.5× 517 1.5× 343 1.2× 363 1.2× 18 1.4k
Jamshid Dehmeshki United Kingdom 23 1.1k 1.0× 158 0.3× 346 1.0× 417 1.4× 196 0.7× 89 1.9k
Xin Yang China 21 1.5k 1.4× 154 0.3× 1.2k 3.5× 1.2k 4.0× 587 2.0× 86 3.1k
Sérgio Pereira Portugal 19 1.2k 1.1× 190 0.3× 1.6k 4.7× 892 3.0× 356 1.2× 34 3.0k
Aurélio Campilho Portugal 28 2.3k 2.2× 1.2k 1.9× 1.7k 4.9× 1.0k 3.6× 223 0.8× 140 3.7k
Weifang Zhu China 23 1.5k 1.4× 805 1.3× 903 2.6× 420 1.4× 688 2.3× 124 2.3k

Countries citing papers authored by Jorge Novo

Since Specialization
Citations

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

Fields of papers citing papers by Jorge Novo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jorge Novo

This figure shows the co-authorship network connecting the top 25 collaborators of Jorge Novo. A scholar is included among the top collaborators of Jorge Novo 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 Jorge Novo. Jorge Novo 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.
Herrero, Pau, et al.. (2025). Inter-expert reliability in multi-field-of-view automatic drusen segmentation analysis using optical coherence tomography. Biomedical Signal Processing and Control. 112. 108476–108476.
2.
García‐Porta, Nery, et al.. (2024). Are artificial intelligence chatbots a reliable source of information about contact lenses?. Contact Lens and Anterior Eye. 47(2). 102130–102130. 3 indexed citations
3.
Hervella, Álvaro S., et al.. (2024). ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration. Medical & Biological Engineering & Computing. 62(12). 3721–3736.
4.
Moura, Joaquim de, et al.. (2024). Efficient clinical decision-making process via AI-based multimodal data fusion: A COVID-19 case study. Heliyon. 10(20). e38642–e38642.
5.
Moura, Joaquim de, et al.. (2023). Automatic simultaneous ciliary muscle segmentation and biomarker extraction in AS-OCT images using deep learning-based approaches. Biomedical Signal Processing and Control. 90. 105851–105851. 3 indexed citations
6.
Hervella, Álvaro S., Lucía Ramos, José Rouco, Jorge Novo, & Marcos Ortega. (2023). Explainable artificial intelligence for the automated assessment of the retinal vascular tortuosity. Medical & Biological Engineering & Computing. 62(3). 865–881. 7 indexed citations
7.
Hervella, Álvaro S., et al.. (2023). Joint keypoint detection and description network for color fundus image registration. Quantitative Imaging in Medicine and Surgery. 13(7). 4540–4562. 6 indexed citations
8.
Moura, Joaquim de, et al.. (2023). Multivendor fully automatic uncertainty management approaches for the intuitive representation of DME fluid accumulations in OCT images. Medical & Biological Engineering & Computing. 61(5). 1209–1224. 3 indexed citations
9.
Gende, Mateo, Joaquim de Moura, José Ignacio Fernández‐Vigo, et al.. (2023). Robust multi-view approaches for retinal layer segmentation in glaucoma patients via transfer learning. Quantitative Imaging in Medicine and Surgery. 13(5). 2846–2859. 10 indexed citations
10.
Carmona, Enrique J., et al.. (2023). Deformable registration of multimodal retinal images using a weakly supervised deep learning approach. Neural Computing and Applications. 35(20). 14779–14797. 6 indexed citations
11.
Moura, Joaquim de, et al.. (2020). Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices. IEEE Access. 8. 195594–195607. 59 indexed citations
12.
Moura, Joaquim de, et al.. (2020). Analysis of Separability of COVID-19 and Pneumonia in Chest X-ray Images by Means of Convolutional Neural Networks. SHILAP Revista de lepidopterología. 31–31. 1 indexed citations
13.
Ramos, Lucía, et al.. (2020). Fully Automatic Retinal Vascular Tortuosity Assessment Integrating Domain-Related Information. SHILAP Revista de lepidopterología. 32–32.
14.
Carmona, Enrique J., et al.. (2020). Modeling, Localization, and Segmentation of the Foveal Avascular Zone on Retinal OCT-Angiography Images. IEEE Access. 8. 152223–152238. 3 indexed citations
15.
Moura, Joaquim de, et al.. (2019). Computerized tool for identification and enhanced visualization of Macular Edema regions using OCT scans.. The European Symposium on Artificial Neural Networks. 1 indexed citations
16.
Rouco, José, et al.. (2019). An end-to-end deep learning approach for simultaneous background modeling and subtraction.. British Machine Vision Conference. 266. 25 indexed citations
17.
Moura, Joaquim de, et al.. (2019). Automatic identification and characterization of the epiretinal membrane in OCT images. Biomedical Optics Express. 10(8). 4018–4018. 12 indexed citations
18.
Moura, Joaquim de, et al.. (2018). Intraretinal fluid identification via enhanced maps using optical coherence tomography images. Biomedical Optics Express. 9(10). 4730–4730. 29 indexed citations
19.
Gonçalves, L.M., Jorge Novo, & Aurélio Campilho. (2016). Feature definition, analysis and selection for lung nodule classification in chest computerized tomography images.. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT). 1 indexed citations
20.
Kerolus, Mena G., et al.. (2015). Pigmented ganglioglioma in a patient with chronic epilepsy and cortical dysplasia. Journal of Clinical Neuroscience. 24. 17–21. 2 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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