Pablo Márquez-Neila

1.7k total citations
28 papers, 912 citations indexed

About

Pablo Márquez-Neila is a scholar working on Computer Vision and Pattern Recognition, Biophysics and Artificial Intelligence. According to data from OpenAlex, Pablo Márquez-Neila has authored 28 papers receiving a total of 912 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 6 papers in Biophysics and 5 papers in Artificial Intelligence. Recurrent topics in Pablo Márquez-Neila's work include Medical Image Segmentation Techniques (8 papers), Advanced Image and Video Retrieval Techniques (5 papers) and Cell Image Analysis Techniques (5 papers). Pablo Márquez-Neila is often cited by papers focused on Medical Image Segmentation Techniques (8 papers), Advanced Image and Video Retrieval Techniques (5 papers) and Cell Image Analysis Techniques (5 papers). Pablo Márquez-Neila collaborates with scholars based in Switzerland, Spain and United States. Pablo Márquez-Neila's co-authors include Pascal Fua, Luis Baumela, Luis Álvarez, Mathieu Salzmann, Mateusz Koziński, Agata Mosinska, Bugra Tekin, Raphael Sznitman, Andrea Mendizábal and Stéphane Cotin and has published in prestigious journals such as The Astrophysical Journal, IEEE Transactions on Pattern Analysis and Machine Intelligence and Scientific Reports.

In The Last Decade

Pablo Márquez-Neila

28 papers receiving 882 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pablo Márquez-Neila Switzerland 14 449 163 158 127 85 28 912
Nantheera Anantrasirichai United Kingdom 18 471 1.0× 175 1.1× 122 0.8× 156 1.2× 54 0.6× 76 1.1k
Μαρία Πέτρου United Kingdom 16 744 1.7× 137 0.8× 94 0.6× 208 1.6× 48 0.6× 76 1.4k
Arnold Meijster Netherlands 9 644 1.4× 111 0.7× 69 0.4× 158 1.2× 72 0.8× 13 1.2k
Jae-Chern Yoo South Korea 11 457 1.0× 89 0.5× 173 1.1× 112 0.9× 47 0.6× 35 1.1k
John M. Gauch United States 18 630 1.4× 111 0.7× 46 0.3× 82 0.6× 23 0.3× 55 1.1k
Nicolas Passat France 18 772 1.7× 281 1.7× 87 0.6× 102 0.8× 36 0.4× 80 1.2k
N. Sarkar India 7 634 1.4× 130 0.8× 92 0.6× 236 1.9× 57 0.7× 7 1.5k
Ali M. Reza United States 5 744 1.7× 248 1.5× 132 0.8× 169 1.3× 47 0.6× 9 1.2k
Fethallah Benmansour Switzerland 14 257 0.6× 285 1.7× 40 0.3× 72 0.6× 66 0.8× 30 826
Yuyan Chao Japan 11 709 1.6× 130 0.8× 58 0.4× 79 0.6× 43 0.5× 52 1.1k

Countries citing papers authored by Pablo Márquez-Neila

Since Specialization
Citations

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

Fields of papers citing papers by Pablo Márquez-Neila

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pablo Márquez-Neila. 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 Pablo Márquez-Neila. The network helps show where Pablo Márquez-Neila may publish in the future.

Co-authorship network of co-authors of Pablo Márquez-Neila

This figure shows the co-authorship network connecting the top 25 collaborators of Pablo Márquez-Neila. A scholar is included among the top collaborators of Pablo Márquez-Neila 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 Pablo Márquez-Neila. Pablo Márquez-Neila 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.
Heng, Kevin, S. Cavuoti, M. Brescia, et al.. (2024). Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models. The Astrophysical Journal. 977(1). 131–131. 3 indexed citations
2.
Wu, Fei, Pablo Márquez-Neila, M.Y. Zheng, Hedyeh Rafii-Tari, & Raphael Sznitman. (2024). Correlation-aware active learning for surgery video segmentation. 1999–2009. 2 indexed citations
3.
Jungo, Alain, et al.. (2023). Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery. International Journal of Computer Assisted Radiology and Surgery. 18(6). 1085–1091. 3 indexed citations
4.
Huber, A, et al.. (2023). Stochastic Segmentation with Conditional Categorical Diffusion Models. 1119–1129. 16 indexed citations
5.
Sampaio, Paulo, Federico Storni, Martin Wartenberg, et al.. (2023). Müller matrix polarimetry for pancreatic tissue characterization. Scientific Reports. 13(1). 16417–16417. 8 indexed citations
6.
Márquez-Neila, Pablo, et al.. (2023). Logical Implications for Visual Question Answering Consistency. 32. 6725–6735. 2 indexed citations
7.
Torbaniuk, O., S. Cavuoti, M. Paolillo, et al.. (2022). ulisse: A tool for one-shot sky exploration and its application for detection of active galactic nuclei. Astronomy and Astrophysics. 666. A171–A171. 4 indexed citations
8.
Munk, Marion R., Thomas Kurmann, Pablo Márquez-Neila, et al.. (2021). Assessment of patient specific information in the wild on fundus photography and optical coherence tomography. Scientific Reports. 11(1). 8621–8621. 16 indexed citations
9.
Kurmann, Thomas, Pablo Márquez-Neila, Max Allan, Sebastián Wolf, & Raphael Sznitman. (2021). Mask then classify: multi-instance segmentation for surgical instruments. International Journal of Computer Assisted Radiology and Surgery. 16(7). 1227–1236. 15 indexed citations
10.
Mendizábal, Andrea, Pablo Márquez-Neila, & Stéphane Cotin. (2019). Simulation of hyperelastic materials in real-time using deep learning. Medical Image Analysis. 59. 101569–101569. 74 indexed citations
11.
Kurmann, Thomas, Pablo Márquez-Neila, Andreas Ebneter, et al.. (2019). Expert-level Automated Biomarker Identification in Optical Coherence Tomography Scans. Scientific Reports. 9(1). 13605–13605. 45 indexed citations
12.
Márquez-Neila, Pablo, et al.. (2019). Patient-attentive sequential strategy for perimetry-based visual field acquisition. Medical Image Analysis. 54. 179–192. 5 indexed citations
13.
Achanta, Radhakrishna, Pablo Márquez-Neila, Pascal Fua, & Sabine Süsstrunk. (2018). Scale-Adaptive Superpixels. Color and Imaging Conference. 26(1). 1–6. 5 indexed citations
14.
Márquez-Neila, Pablo, et al.. (2018). A domain-adaptive two-stream U-Net for electron microscopy image segmentation. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 400–404. 40 indexed citations
15.
Mosinska, Agata, Pablo Márquez-Neila, Mateusz Koziński, & Pascal Fua. (2018). Beyond the Pixel-Wise Loss for Topology-Aware Delineation. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 3136–3145. 162 indexed citations
16.
Tekin, Bugra, Pablo Márquez-Neila, Mathieu Salzmann, & Pascal Fua. (2017). Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 3961–3970. 147 indexed citations
17.
Tekin, Bugra, Pablo Márquez-Neila, Mathieu Salzmann, & Pascal Fua. (2016). Fusing 2D Uncertainty and 3D Cues for Monocular Body Pose Estimation.. arXiv (Cornell University). 13 indexed citations
18.
Lucchi, Aurélien, Pablo Márquez-Neila, Carlos Becker, et al.. (2014). Learning Structured Models for Segmentation of 2-D and 3-D Imagery. IEEE Transactions on Medical Imaging. 34(5). 1096–1110. 18 indexed citations
19.
Márquez-Neila, Pablo, et al.. (2014). A Comparative Study of Feature Descriptors for Mitochondria and Synapse Segmentation. 3215–3220. 5 indexed citations
20.
Álvarez, Luis, et al.. (2010). Morphological snakes. Acceda (Universidad de Las Palmas de Gran Canaria). 2197–2202. 40 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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