Adrián Colomer

1.9k total citations
56 papers, 796 citations indexed

About

Adrián Colomer is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Adrián Colomer has authored 56 papers receiving a total of 796 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Artificial Intelligence, 23 papers in Computer Vision and Pattern Recognition and 17 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Adrián Colomer's work include AI in cancer detection (23 papers), Retinal Imaging and Analysis (14 papers) and Digital Imaging for Blood Diseases (12 papers). Adrián Colomer is often cited by papers focused on AI in cancer detection (23 papers), Retinal Imaging and Analysis (14 papers) and Digital Imaging for Blood Diseases (12 papers). Adrián Colomer collaborates with scholars based in Spain, Norway and France. Adrián Colomer's co-authors include Valery Naranjo, Sandra Morales, Julio Silva-Rodríguez, Gabriel García, Alejandro F. Frangi, Rocío del Amor, Yanwu Xu, Andres Diaz‐Pinto, Jorge Igual and Kjersti Engan and has published in prestigious journals such as Scientific Reports, IEEE Access and IEEE Transactions on Medical Imaging.

In The Last Decade

Adrián Colomer

52 papers receiving 760 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Adrián Colomer Spain 16 440 323 293 226 114 56 796
Guilherme Aresta Portugal 11 681 1.5× 662 2.0× 336 1.1× 106 0.5× 86 0.8× 23 979
Tahir Mahmood South Korea 13 342 0.8× 232 0.7× 192 0.7× 88 0.4× 60 0.5× 30 620
Sertan Serte Cyprus 15 733 1.7× 447 1.4× 263 0.9× 139 0.6× 106 0.9× 36 1.0k
Teresa Araújo Portugal 9 647 1.5× 662 2.0× 346 1.2× 109 0.5× 85 0.7× 20 953
Chetan L. Srinidhi India 6 368 0.8× 358 1.1× 266 0.9× 119 0.5× 75 0.7× 7 602
Shujun Wang China 10 407 0.9× 306 0.9× 263 0.9× 133 0.6× 57 0.5× 30 694
John Arévalo Colombia 13 483 1.1× 705 2.2× 367 1.3× 45 0.2× 121 1.1× 30 1.0k
Luyang Luo Hong Kong 11 431 1.0× 308 1.0× 135 0.5× 146 0.6× 24 0.2× 26 706
P. C. Siddalingaswamy India 12 216 0.5× 223 0.7× 165 0.6× 114 0.5× 244 2.1× 34 538
Carson Lam United States 13 606 1.4× 275 0.9× 179 0.6× 290 1.3× 38 0.3× 25 1.1k

Countries citing papers authored by Adrián Colomer

Since Specialization
Citations

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

Fields of papers citing papers by Adrián Colomer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Adrián Colomer

This figure shows the co-authorship network connecting the top 25 collaborators of Adrián Colomer. A scholar is included among the top collaborators of Adrián Colomer 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 Adrián Colomer. Adrián Colomer 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.
Colomer, Adrián, et al.. (2025). The puzzling Spitz tumours: is artificial intelligence the key to their understanding?. Histopathology. 87(4). 489–502.
2.
Colomer, Adrián, et al.. (2025). Enhancing Image Retrieval Performance With Generative Models in Siamese Networks. IEEE Journal of Biomedical and Health Informatics. 29(7). 4956–4968. 1 indexed citations
3.
Colomer, Adrián, Katharina Wiedemeyer, Emiel A. M. Janssen, et al.. (2024). Histological interpretation of spitzoid tumours: an extensive machine learning‐based concordance analysis for improving decision making. Histopathology. 85(1). 155–170. 3 indexed citations
4.
Colomer, Adrián, et al.. (2024). Optimizing Deep Learning Models for Edge Computing in Histopathology: Bridging the Gap to Clinical Practice. Procedia Computer Science. 246. 2549–2557. 1 indexed citations
5.
Fuentes-Hurtado, Félix, et al.. (2023). Improving the quality of image generation in art with top-k training and cyclic generative methods. Scientific Reports. 13(1). 17764–17764. 3 indexed citations
6.
Wang, Yuandou, et al.. (2023). WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval. Bioengineering. 10(10). 1144–1144. 6 indexed citations
7.
Wang, Yuandou, Adrián Colomer, Jorge Igual, et al.. (2023). Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions. Applied Sciences. 13(2). 919–919. 4 indexed citations
8.
Amor, Rocío del, et al.. (2023). A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models. Scientific Data. 10(1). 704–704. 5 indexed citations
9.
Bori, Lorena, et al.. (2023). Deep learning system for classification of ploidy status using time-lapse videos. PubMed. 4(3). 211–218. 4 indexed citations
10.
García, Gabriel, Rocío del Amor, Adrián Colomer, et al.. (2021). Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored\nPrototypical Neural Networks. RiuNet (Politechnical University of Valencia). 19 indexed citations
11.
García, Gabriel, et al.. (2021). A novel self-learning framework for bladder cancer grading using histopathological images. RiuNet (Politechnical University of Valencia). 10 indexed citations
12.
Colomer, Adrián, et al.. (2021). Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity. Entropy. 23(7). 898–898. 3 indexed citations
13.
Pueo, Basílio, José J. López, José M. Mossi, Adrián Colomer, & Jose Manuel Jiménez-Olmedo. (2021). Video-Based System for Automatic Measurement of Barbell Velocity in Back Squat. Sensors. 21(3). 925–925. 8 indexed citations
14.
García, Gabriel, Adrián Colomer, & Valery Naranjo. (2020). Glaucoma Detection from Raw SD-OCT Volumes: A Novel Approach Focused on Spatial Dependencies. Computer Methods and Programs in Biomedicine. 200. 105855–105855. 37 indexed citations
15.
Amor, Rocío del, Sandra Morales, Adrián Colomer, et al.. (2020). Automatic Segmentation of Epidermis and Hair Follicles in Optical Coherence Tomography Images of Normal Skin by Convolutional Neural Networks. Frontiers in Medicine. 7. 220–220. 29 indexed citations
16.
Morales, Sandra, Adrián Colomer, José M. Mossi, et al.. (2020). Retinal layer segmentation in rodent OCT images: Local intensity profiles & fully convolutional neural networks. Computer Methods and Programs in Biomedicine. 198. 105788–105788. 9 indexed citations
17.
Silva-Rodríguez, Julio, et al.. (2020). Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection. Computer Methods and Programs in Biomedicine. 195. 105637–105637. 84 indexed citations
18.
García, Gabriel, Adrián Colomer, & Valery Naranjo. (2019). First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning. Entropy. 21(4). 356–356. 15 indexed citations
19.
Diaz‐Pinto, Andres, Adrián Colomer, Valery Naranjo, et al.. (2019). Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment. IEEE Transactions on Medical Imaging. 38(9). 2211–2218. 148 indexed citations
20.
Colomer, Adrián, et al.. (2018). Evaluation of fractal dimension effectiveness for damage detection in retinal background. Journal of Computational and Applied Mathematics. 337. 341–353. 9 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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