Pedro Henriques Abreu

2.9k total citations · 1 hit paper
82 papers, 1.7k citations indexed

About

Pedro Henriques Abreu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Pedro Henriques Abreu has authored 82 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Artificial Intelligence, 18 papers in Computer Vision and Pattern Recognition and 10 papers in Information Systems. Recurrent topics in Pedro Henriques Abreu's work include Machine Learning and Data Classification (11 papers), Imbalanced Data Classification Techniques (9 papers) and AI in cancer detection (9 papers). Pedro Henriques Abreu is often cited by papers focused on Machine Learning and Data Classification (11 papers), Imbalanced Data Classification Techniques (9 papers) and AI in cancer detection (9 papers). Pedro Henriques Abreu collaborates with scholars based in Portugal, Spain and Switzerland. Pedro Henriques Abreu's co-authors include Miriam Seoane Santos, João Santos, J. Soares, Pedro J. García-Laencina, Hélder Araújo, Miguel Henriques Abreu, Daniel Castro Silva, Inês Domingues, Hugo Duarte and Tiago Cruz and has published in prestigious journals such as Journal of Clinical Oncology, International Journal of Cancer and Expert Systems with Applications.

In The Last Decade

Pedro Henriques Abreu

74 papers receiving 1.7k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier]

2018 277 citations Miriam Seoane Santos, J. Soares et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pedro Henriques Abreu Portugal	20	895	270	175	153	149	82	1.7k
Wei‐Chao Lin Taiwan	21	1.3k 1.4×	175 0.6×	302 1.7×	237 1.5×	125 0.8×	52	2.4k
José M. Jerez Spain	22	718 0.8×	134 0.5×	240 1.4×	150 1.0×	68 0.5×	71	1.9k
Dharmendra Singh Rajput India	17	776 0.9×	223 0.8×	290 1.7×	271 1.8×	134 0.9×	95	2.1k
Firuz Kamalov United Arab Emirates	21	826 0.9×	107 0.4×	167 1.0×	113 0.7×	105 0.7×	89	2.2k
Yin Lou United States	12	1.1k 1.2×	93 0.3×	209 1.2×	105 0.7×	469 3.1×	19	2.2k
Vili Podgorelec Slovenia	20	938 1.0×	117 0.4×	197 1.1×	158 1.0×	136 0.9×	115	2.2k
Hamza Turabieh Saudi Arabia	31	1.3k 1.5×	215 0.8×	344 2.0×	85 0.6×	84 0.6×	85	2.8k
Pedro J. García-Laencina Spain	12	845 0.9×	65 0.2×	201 1.1×	185 1.2×	177 1.2×	20	1.7k
Ioannis E. Livieris Greece	22	680 0.8×	129 0.5×	210 1.2×	79 0.5×	200 1.3×	76	2.0k

Countries citing papers authored by Pedro Henriques Abreu

Since Specialization

Citations

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

Fields of papers citing papers by Pedro Henriques Abreu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pedro Henriques Abreu

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

All Works

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20 of 20 papers shown

Santos, Miriam Seoane, et al.. (2025). The Role of Deep Learning in Medical Image Inpainting: A Systematic Review. 6(3). 1–24.

Santos, Miriam Seoane, et al.. (2025). Pycol: A Python package for dataset complexity measures. Neurocomputing. 640. 130311–130311.

Lorena, Ana Carolina, et al.. (2025). Studying the robustness of data imputation methodologies against adversarial attacks. Computers & Security. 157. 104574–104574. 1 indexed citations

Guo, Jielong, et al.. (2025). LogicMix: Sample mixing data augmentation for multi-label image classification with partial labels. Pattern Recognition. 171. 112186–112186.

Guo, Jielong, et al.. (2025). Enhancing the robustness of solar photovoltaic fault classification based on depthwise separable group equivariant CNNs. Alexandria Engineering Journal. 127. 486–499.

Salazar, Teresa, João Gama, Hélder Araújo, & Pedro Henriques Abreu. (2025). Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning. IEEE Transactions on Neural Networks and Learning Systems. 37(1). 161–175. 2 indexed citations

Santos, Miriam Seoane, et al.. (2025). mdatagen: A python library for the artificial generation of missing data. Neurocomputing. 625. 129478–129478. 2 indexed citations

Santos, Miriam Seoane, et al.. (2025). A Label Propagation Approach for Missing Data Imputation. IEEE Access. 13. 65925–65938.

Santos, Miriam Seoane, et al.. (2024). Enhancing mammography: a comprehensive review of computer methods for improving image quality. PubMed. 6(4). 42002–42002. 3 indexed citations

10.

Abreu, Miguel Henriques, et al.. (2023). Bone Metastases Detection in Patients with Breast Cancer: Does Bone Scintigraphy Add Information to PET/CT?. The Oncologist. 28(8). e600–e605. 10 indexed citations

11.

Santos, Miriam Seoane, Pedro Henriques Abreu, Alberto Fernández, Julián Luengo, & João Santos. (2022). The impact of heterogeneous distance functions on missing data imputation and classification performance. Engineering Applications of Artificial Intelligence. 111. 104791–104791. 16 indexed citations

12.

Graziani, Mara, Davide Calvaresi, Mor Vered, et al.. (2022). A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences. Artificial Intelligence Review. 56(4). 3473–3504. 65 indexed citations

13.

Santos, Miriam Seoane, Pedro Henriques Abreu, Nathalie Japkowicz, Alberto Fernández, & João Santos. (2022). A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research. Information Fusion. 89. 228–253. 51 indexed citations

14.

Santos, Miriam Seoane, Pedro Henriques Abreu, Nathalie Japkowicz, et al.. (2022). On the joint-effect of class imbalance and overlap: a critical review. Artificial Intelligence Review. 55(8). 6207–6275. 70 indexed citations

15.

Salazar, Teresa, Miriam Seoane Santos, Hélder Araújo, & Pedro Henriques Abreu. (2021). FAWOS: Fairness-Aware Oversampling Algorithm Based on Distributions of Sensitive Attributes. IEEE Access. 9. 81370–81379. 12 indexed citations

16.

Rodrigues, Pedro Pereira, et al.. (2020). Missing Image Data Imputation using Variational Autoencoders with Weighted Loss.. The European Symposium on Artificial Neural Networks. 475–480. 1 indexed citations

17.

Santos, Miriam Seoane, et al.. (2019). Generating Synthetic Missing Data: A Review by Missing Mechanism. IEEE Access. 7. 11651–11667. 59 indexed citations

18.

Domingues, Inês, et al.. (2018). Interpreting deep learning models for ordinal problems.. The European Symposium on Artificial Neural Networks. 4 indexed citations

19.

Abreu, Pedro Henriques, Daniel Castro Silva, Michael Schumacher, et al.. (2016). Special Issue JOMS – Journal of Medical Systems, 2016 on Agent-Empowered HealthCare Systems. Journal of Medical Systems. 40(4). 93–93. 1 indexed citations

20.

Abreu, Pedro Henriques, et al.. (2010). Knowledge representation in soccer domain: An ontology development. Iberian Conference on Information Systems and Technologies. 1–6. 3 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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