John Arévalo

2.4k total citations · 1 hit paper
30 papers, 1.0k citations indexed

About

John Arévalo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, John Arévalo has authored 30 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Vision and Pattern Recognition, 12 papers in Artificial Intelligence and 10 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in John Arévalo's work include AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Cell Image Analysis Techniques (6 papers). John Arévalo is often cited by papers focused on AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Cell Image Analysis Techniques (6 papers). John Arévalo collaborates with scholars based in Colombia, United States and Mexico. John Arévalo's co-authors include Fabio A. González, Ángel Cruz-Roa, Raúl Ramos-Pollán, José Luís Oliveira, Miguel Guevara, Anant Madabhushi, Thamar Solorio, Manuel Montes-y-Gómez, Oscar Perdómo and Eduardo Romero and has published in prestigious journals such as Nature Communications, PLoS Computational Biology and Physics in Medicine and Biology.

In The Last Decade

John Arévalo

28 papers receiving 1.0k citations

Hit Papers

align trajectories

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
John Arévalo Colombia	13	705	483	367	121	93	30	1.0k
Sen Yang China	16	657 0.9×	493 1.0×	371 1.0×	115 1.0×	86 0.9×	57	1.2k
Guilherme Aresta Portugal	11	662 0.9×	681 1.4×	336 0.9×	86 0.7×	93 1.0×	23	979
Teresa Araújo Portugal	9	662 0.9×	647 1.3×	346 0.9×	85 0.7×	101 1.1×	20	953
Veronika Cheplygina Netherlands	14	593 0.8×	381 0.8×	464 1.3×	85 0.7×	60 0.6×	24	1.3k
Changhao Sun China	13	639 0.9×	423 0.9×	405 1.1×	134 1.1×	96 1.0×	23	1.0k
Dimitris Samaras United States	12	684 1.0×	427 0.9×	422 1.1×	145 1.2×	59 0.6×	28	1.0k
Ali Mohammad Alqudah Jordan	21	418 0.6×	415 0.9×	242 0.7×	162 1.3×	53 0.6×	64	1.2k
Ebrahim Mohammed Senan Saudi Arabia	21	609 0.9×	427 0.9×	328 0.9×	202 1.7×	316 3.4×	31	1.3k
Justin Ker Singapore	5	513 0.7×	537 1.1×	356 1.0×	46 0.4×	214 2.3×	6	1.3k
José Rouco Spain	18	651 0.9×	899 1.9×	524 1.4×	83 0.7×	101 1.1×	49	1.5k

Countries citing papers authored by John Arévalo

Since Specialization

Citations

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

Fields of papers citing papers by John Arévalo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Arévalo

This figure shows the co-authorship network connecting the top 25 collaborators of John Arévalo. A scholar is included among the top collaborators of John Arévalo 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 John Arévalo. John Arévalo 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

Kalinin, Alexandr A., John Arévalo, Loan Vulliard, et al.. (2025). A versatile information retrieval framework for evaluating profile strength and similarity. Nature Communications. 16(1). 5181–5181. 2 indexed citations

Sivagurunathan, Suganya, Patrick J. Byrne, John Arévalo, et al.. (2025). Alternate dyes for image-based profiling assays. SLAS DISCOVERY. 36. 100268–100268.

Arévalo, John, et al.. (2024). A contrastive weakly supervised learning to characterize malignant prostate lesions in BP-MRI. Biomedical Signal Processing and Control. 96. 106584–106584. 1 indexed citations

Arévalo, John, et al.. (2024). Evaluating batch correction methods for image-based cell profiling. Nature Communications. 15(1). 6516–6516. 19 indexed citations

Arévalo, John, et al.. (2024). MOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction. 140320–140333. 1 indexed citations

Arévalo, John, et al.. (2024). Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning. PLoS Computational Biology. 20(11). e1012547–e1012547. 2 indexed citations

Arévalo, John, et al.. (2022). An inception-based deep multiparametric net to classify clinical significance MRI regions of prostate cancer. Physics in Medicine and Biology. 67(22). 225004–225004. 5 indexed citations

Arévalo, John, Thamar Solorio, Manuel Montes-y-Gómez, & Fabio A. González. (2020). Gated multimodal networks. Neural Computing and Applications. 32(14). 10209–10228. 60 indexed citations

Arévalo, John, Thamar Solorio, Manuel Montes-y-Gómez, & Fabio A. González. (2017). Gated Multimodal Units for Information Fusion. arXiv (Cornell University). 21 indexed citations

10.

Perdómo, Oscar, Sebastian Otálora, Francisco Javier Iglesias Rodríguez, John Arévalo, & Fabio A. González. (2016). A Novel Machine Learning Model Based on Exudate Localization to Detect Diabetic Macular Edema. 137–144. 37 indexed citations

11.

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 →

12.

Vanegas, Jorge A., et al.. (2015). INAOE-UNAL at ImageCLEF 2015: Scalable Concept Image Annotation. CLEF (Working Notes). 2 indexed citations

13.

Arévalo, John, et al.. (2015). An unsupervised feature learning framework for basal cell carcinoma image analysis. Artificial Intelligence in Medicine. 64(2). 131–145. 66 indexed citations

14.

Cruz-Roa, Ángel, John Arévalo, Ajay Basavanhally, Anant Madabhushi, & Fabio A. González. (2015). A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9287. 92870G–92870G. 9 indexed citations

15.

Cruz-Roa, Ángel, John Arévalo, Alexander R. Judkins, Anant Madabhushi, & Fabio A. González. (2015). A method for medulloblastoma tumor differentiation based on convolutional neural networks and transfer learning. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9681. 968103–968103. 19 indexed citations

16.

Vanegas, Jorge A., et al.. (2014). MindLab at ImageCLEF 2014: Scalable Concept Image Annotation.. CLEF (Working Notes). 404–410. 3 indexed citations

17.

Arévalo, John, et al.. (2014). HISTOPATHOLOGY IMAGE REPRESENTATION FOR AUTOMATIC ANALYSIS: A STATE-OF-THE-ART REVIEW. Revista Med. 22(2). 79–91. 18 indexed citations

18.

Arévalo, John, et al.. (2014). Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte. Revista Med. 22(2). 79–79. 28 indexed citations

19.

Cruz-Roa, Ángel, John Arévalo, Anant Madabhushi, & Fabio A. González. (2013). A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection. Lecture notes in computer science. 16(Pt 2). 403–410. 267 indexed citations

20.

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

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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