Pedro C. Neto

522 total citations
20 papers, 206 citations indexed

About

Pedro C. Neto is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Pedro C. Neto has authored 20 papers receiving a total of 206 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 8 papers in Signal Processing and 7 papers in Artificial Intelligence. Recurrent topics in Pedro C. Neto's work include Face recognition and analysis (10 papers), Biometric Identification and Security (8 papers) and AI in cancer detection (7 papers). Pedro C. Neto is often cited by papers focused on Face recognition and analysis (10 papers), Biometric Identification and Security (8 papers) and AI in cancer detection (7 papers). Pedro C. Neto collaborates with scholars based in Portugal, Germany and Austria. Pedro C. Neto's co-authors include Jaime S. Cardoso, Ana F. Sequeira, Naser Damer, Diana Montezuma, Fadi Boutros, Isabel Pinto, João Ribeiro Pinto, Marco Huber, Tiago Gonçalves and Philipp Terhörst and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Access.

In The Last Decade

Pedro C. Neto

17 papers receiving 203 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pedro C. Neto Portugal 8 107 101 63 47 32 20 206
Yipei Wang United States 11 153 1.4× 126 1.2× 84 1.3× 38 0.8× 29 0.9× 17 316
Bruno Korbar United States 6 142 1.3× 243 2.4× 182 2.9× 56 1.2× 106 3.3× 7 388
Hamid Behravan Finland 9 59 0.6× 177 1.8× 37 0.6× 105 2.2× 20 0.6× 18 294
Fandong Zhang China 10 94 0.9× 119 1.2× 112 1.8× 45 1.0× 8 0.3× 22 253
Maximilian Ilse Netherlands 5 74 0.7× 96 1.0× 58 0.9× 10 0.2× 7 0.2× 7 166
Menglin Jiang China 9 206 1.9× 118 1.2× 54 0.9× 23 0.5× 9 0.3× 17 270
Victor Pomponiu Italy 10 175 1.6× 139 1.4× 40 0.6× 11 0.2× 104 3.3× 21 319
Zuojin Hu China 6 65 0.6× 81 0.8× 68 1.1× 7 0.1× 10 0.3× 19 217
Connah Kendrick United Kingdom 6 120 1.1× 115 1.1× 24 0.4× 12 0.3× 106 3.3× 16 295
Ehab A. AlBadawy United States 8 69 0.6× 177 1.8× 168 2.7× 47 1.0× 10 0.3× 9 331

Countries citing papers authored by Pedro C. Neto

Since Specialization
Citations

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

Fields of papers citing papers by Pedro C. Neto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pedro C. Neto. 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 Pedro C. Neto. The network helps show where Pedro C. Neto may publish in the future.

Co-authorship network of co-authors of Pedro C. Neto

This figure shows the co-authorship network connecting the top 25 collaborators of Pedro C. Neto. A scholar is included among the top collaborators of Pedro C. Neto 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 Pedro C. Neto. Pedro C. Neto 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.
Huber, Marco, et al.. (2025). FX-MAD: Frequency-Domain Explainability and Explainability-Driven Unsupervised Detection of Face Morphing Attacks. TUbilio (Technical University of Darmstadt). 718–728.
2.
Neto, Pedro C., Diana Montezuma, João Monteiro, et al.. (2024). An interpretable machine learning system for colorectal cancer diagnosis from pathology slides. npj Precision Oncology. 8(1). 56–56. 15 indexed citations
3.
4.
Neto, Pedro C., et al.. (2024). Model compression techniques in biometrics applications: A survey. Information Fusion. 114. 102657–102657. 8 indexed citations
5.
Montezuma, Diana, et al.. (2023). A CAD system for automatic dysplasia grading on H&E cervical whole-slide images. Scientific Reports. 13(1). 3970–3970. 5 indexed citations
6.
Montezuma, Diana, et al.. (2023). Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers. Modern Pathology. 36(4). 100086–100086. 20 indexed citations
7.
Sadeghi, Maryam, Pedro C. Neto, Enrica Paradiso, et al.. (2023). Localization and Registration of 2D Histological Mouse Brain Images in 3D Atlas Space. Neuroinformatics. 21(3). 615–630. 3 indexed citations
8.
Neto, Pedro C., et al.. (2023). Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 955–959. 4 indexed citations
9.
Neto, Pedro C., Ana F. Sequeira, Jaime S. Cardoso, & Philipp Terhörst. (2023). PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric Face Recognition. 1021–1029. 7 indexed citations
10.
11.
Moreira, Ana, et al.. (2022). Quality Control in Digital Pathology: Automatic Fragment Detection and Counting. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022. 588–593. 4 indexed citations
12.
Neto, Pedro C., Tiago Gonçalves, Marco Huber, et al.. (2022). OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 1–5. 7 indexed citations
13.
Neto, Pedro C., João Ribeiro Pinto, Fadi Boutros, et al.. (2022). Beyond Masks: On the Generalization of Masked Face Recognition Models to Occluded Face Recognition. IEEE Access. 10. 86222–86233. 18 indexed citations
14.
Sadeghi, Maryam, Pedro C. Neto, Enrica Paradiso, et al.. (2022). Automatic 2D to 3D localization of histological mouse brain sections in the reference atlas using deep learning. Data Archiving and Networked Services (DANS). 94–94. 1 indexed citations
15.
Huber, Marco, Fadi Boutros, Kiran Raja, et al.. (2022). SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 1–10. 23 indexed citations
16.
Neto, Pedro C., Fadi Boutros, João Ribeiro Pinto, et al.. (2022). OCFR 2022: Competition on Occluded Face Recognition from Synthetically Generated Structure-Aware Occlusions. Istanbul Technical University Academic Open Archive (Istanbul Technical University). 1–9. 5 indexed citations
17.
Neto, Pedro C., et al.. (2022). iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images. Cancers. 14(10). 2489–2489. 23 indexed citations
18.
Neto, Pedro C., et al.. (2021). CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance. Scientific Reports. 11(1). 14358–14358. 34 indexed citations
19.
Neto, Pedro C., Fadi Boutros, João Ribeiro Pinto, et al.. (2021). FocusFace: Multi-task Contrastive Learning for Masked Face Recognition. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 1–8. 27 indexed citations
20.
Neto, Pedro C., et al.. (2008). Cisto de Tarlov: definição, etiopatogenia, propedêutica e linhas de tratamento.. SHILAP Revista de lepidopterología.

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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