Raúl Ramos-Pollán

1.2k total citations · 1 hit paper
24 papers, 748 citations indexed

About

Raúl Ramos-Pollán is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Raúl Ramos-Pollán has authored 24 papers receiving a total of 748 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 10 papers in Computer Vision and Pattern Recognition and 5 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Raúl Ramos-Pollán's work include AI in cancer detection (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Advanced Steganography and Watermarking Techniques (4 papers). Raúl Ramos-Pollán is often cited by papers focused on AI in cancer detection (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Advanced Steganography and Watermarking Techniques (4 papers). Raúl Ramos-Pollán collaborates with scholars based in Colombia, Portugal and Spain. Raúl Ramos-Pollán's co-authors include Miguel Guevara, Fabio A. González, John Arévalo, José Luís Oliveira, Reinel Tabares-Soto, Gustavo Isaza, Juan C. Duque, Ana Beatriz Acevedo, Alejandro Betancourt and Isabel Ramos and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Building and Environment.

In The Last Decade

Raúl Ramos-Pollán

22 papers receiving 722 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.

Representation learning for mammography mass lesion classification with convolutional neural networks

2016 317 citations John Arévalo, Fabio A. González et al. Computer Methods and Programs in Biomedicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Raúl Ramos-Pollán Colombia	9	453	327	256	68	67	24	748
Anabia Sohail Pakistan	14	320 0.7×	290 0.9×	204 0.8×	36 0.5×	71 1.1×	23	732
Rahimeh Rouhi France	6	278 0.6×	195 0.6×	188 0.7×	23 0.3×	90 1.3×	15	455
Dengao Li China	15	404 0.9×	272 0.8×	127 0.5×	70 1.0×	79 1.2×	91	807
Ameer Hamza Pakistan	14	276 0.6×	236 0.7×	108 0.4×	35 0.5×	85 1.3×	46	617
Leandro Alves Neves Brazil	16	520 1.1×	249 0.8×	390 1.5×	31 0.5×	41 0.6×	85	780
Changmiao Wang China	13	178 0.4×	222 0.7×	290 1.1×	114 1.7×	94 1.4×	69	797
Changhee Han Japan	10	316 0.7×	249 0.8×	325 1.3×	63 0.9×	113 1.7×	27	762
Claudio Marrocco Italy	14	380 0.8×	183 0.6×	257 1.0×	21 0.3×	38 0.6×	46	656
Huiling Lu China	9	291 0.6×	272 0.8×	151 0.6×	57 0.8×	58 0.9×	43	682

Countries citing papers authored by Raúl Ramos-Pollán

Since Specialization

Citations

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

Fields of papers citing papers by Raúl Ramos-Pollán

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Raúl Ramos-Pollán. 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 Raúl Ramos-Pollán. The network helps show where Raúl Ramos-Pollán may publish in the future.

Co-authorship network of co-authors of Raúl Ramos-Pollán

This figure shows the co-authorship network connecting the top 25 collaborators of Raúl Ramos-Pollán. A scholar is included among the top collaborators of Raúl Ramos-Pollán 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 Raúl Ramos-Pollán. Raúl Ramos-Pollán 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

Montoya, Alejandro, et al.. (2025). Machine-learning component for multi-start metaheuristics to solve the capacitated vehicle routing problem. Applied Soft Computing. 173. 112916–112916. 1 indexed citations

Bravo-Ortíz, Mario Alejandro, et al.. (2024). Preprocessing Strategy to Improve the Performance of Convolutional Neural Networks Applied to Steganalysis in the Spatial Domain. Journal of Advances in Information Technology. 15(1). 33–39. 1 indexed citations

Allen, Matthew R., et al.. (2024). M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data. 104694–104723.

Montoya, Alejandro, et al.. (2023). A two-stage data-driven metaheuristic to predict last-mile delivery route sequences. Engineering Applications of Artificial Intelligence. 125. 106653–106653. 6 indexed citations

Ramos-Pollán, Raúl, et al.. (2022). Synthetic data generation with deep generative models to enhance predictive tasks in trading strategies. Research in International Business and Finance. 62. 101747–101747. 7 indexed citations

Ramos-Pollán, Raúl, et al.. (2022). Towards reduction of expert bias on Gleason score classification via a semi-supervised deep learning strategy. 2. 93–93. 1 indexed citations

Tabares-Soto, Reinel, Harold Brayan Arteaga-Arteaga, Mario Alejandro Bravo-Ortíz, et al.. (2021). GBRAS-Net: A Convolutional Neural Network Architecture for Spatial Image Steganalysis. IEEE Access. 9. 14340–14350. 60 indexed citations

Acevedo, Ana Beatriz, et al.. (2020). Automatic detection of building typology using deep learning methods on street level images. Building and Environment. 177. 106805–106805. 78 indexed citations

Acevedo, Ana Beatriz, et al.. (2020). Use of deep learning models in street-level images to classify one-story unreinforced masonry buildings based on roof diaphragms. Building and Environment. 189. 107517–107517. 12 indexed citations

10.

Tabares-Soto, Reinel, Raúl Ramos-Pollán, & Gustavo Isaza. (2019). Deep Learning Applied to Steganalysis of Digital Images: A Systematic Review. IEEE Access. 7. 68970–68990. 52 indexed citations

11.

Ramos-Pollán, Raúl, et al.. (2017). Exploring Alzheimer's anatomical patterns through convolutional networks. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10160. 101600Z–101600Z. 4 indexed citations

12.

Ramos-Pollán, Raúl, et al.. (2016). Assessing the behavior of machine learning methods to predict the activity of antimicrobial peptides. SHILAP Revista de lepidopterología. 26(44). 167–180. 1 indexed citations

13.

Arévalo, John, Fabio A. González, Raúl Ramos-Pollán, José Luís Oliveira, & Miguel Guevara. (2016). Representation learning for mammography mass lesion classification with convolutional neural networks. Computer Methods and Programs in Biomedicine. 127. 248–257. 317 indexed citations breakdown →

14.

Ramos-Pollán, Raúl, et al.. (2016). Effective training of convolutional neural networks with small, specialized datasets. Journal of Intelligent & Fuzzy Systems. 32(2). 1333–1342. 5 indexed citations

15.

Vanegas, Jorge A., Juan Carlos Caicedo, Jorge E. Camargo, Raúl Ramos-Pollán, & Fabio A. González. (2012). Bioingenium at ImageCLEF 2012: Text and Visual Indexing for Medical Images.. 5 indexed citations

16.

Ramos-Pollán, Raúl, Fabio A. González, Juan Carlos Caicedo, et al.. (2012). BIGS: A framework for large-scale image processing and analysis over distributed and heterogeneous computing resources. 1–8. 8 indexed citations

17.

Ramos-Pollán, Raúl, Ángel Cruz-Roa, & Fabio A. González. (2012). A framework for high performance image analysis pipelines. 8. 1–6. 1 indexed citations

18.

Ramos-Pollán, Raúl, Miguel Guevara, & Eugénio Oliveira. (2011). A Software Framework for Building Biomedical Machine Learning Classifiers through Grid Computing Resources. Journal of Medical Systems. 36(4). 2245–2257. 11 indexed citations

19.

Ramos-Pollán, Raúl, et al.. (2011). Discovering Mammography-based Machine Learning Classifiers for Breast Cancer Diagnosis. Journal of Medical Systems. 36(4). 2259–2269. 66 indexed citations

20.

Ramos-Pollán, Raúl, et al.. (2010). Grid infrastructures for developing mammography CAD systems. PubMed. 2010. 3467–3470. 7 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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