Arnau Oliver

5.8k total citations
107 papers, 3.6k citations indexed

About

Arnau Oliver is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Arnau Oliver has authored 107 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 69 papers in Computer Vision and Pattern Recognition, 43 papers in Artificial Intelligence and 31 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Arnau Oliver's work include Medical Image Segmentation Techniques (49 papers), AI in cancer detection (40 papers) and Multiple Sclerosis Research Studies (17 papers). Arnau Oliver is often cited by papers focused on Medical Image Segmentation Techniques (49 papers), AI in cancer detection (40 papers) and Multiple Sclerosis Research Studies (17 papers). Arnau Oliver collaborates with scholars based in Spain, France and United Kingdom. Arnau Oliver's co-authors include Xavier Lladó, Jordi Freixenet, Mariano Cabezas, Sergi Valverde, Joan C. Vilanova, Reyer Zwiggelaar, Robert Martí, Àlex Rovira, Joan Martı́ and Lluís Ramió‐Torrentà and has published in prestigious journals such as SHILAP Revista de lepidopterología, NeuroImage and Scientific Reports.

In The Last Decade

Arnau Oliver

103 papers receiving 3.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Arnau Oliver Spain 32 1.8k 1.4k 1.3k 724 525 107 3.6k
Xavier Lladó Spain 33 2.3k 1.3× 976 0.7× 1.2k 0.9× 701 1.0× 310 0.6× 130 4.1k
Jordi Freixenet Spain 25 1.4k 0.8× 1.1k 0.8× 926 0.7× 276 0.4× 471 0.9× 71 2.6k
Qianjin Feng China 41 2.0k 1.1× 1.2k 0.8× 2.9k 2.2× 1.3k 1.8× 410 0.8× 234 5.7k
Robert Martí Spain 25 1.4k 0.8× 2.0k 1.5× 1.8k 1.3× 295 0.4× 693 1.3× 106 3.3k
Aaron Carass United States 35 1.4k 0.8× 441 0.3× 2.3k 1.7× 399 0.6× 172 0.3× 184 4.0k
Tal Arbel Canada 25 1.5k 0.8× 661 0.5× 987 0.7× 355 0.5× 143 0.3× 97 2.8k
Wei Yang China 32 1.5k 0.8× 923 0.7× 1.8k 1.4× 1.1k 1.5× 327 0.6× 175 3.7k
Michel Bilello United States 24 1.2k 0.7× 668 0.5× 1.7k 1.3× 1.2k 1.7× 196 0.4× 60 3.5k
Ashirbani Saha Canada 24 506 0.3× 890 0.6× 1.3k 1.0× 289 0.4× 230 0.4× 69 2.3k
Michael R. Kaus United States 18 1.2k 0.7× 351 0.3× 1.6k 1.2× 366 0.5× 594 1.1× 38 3.4k

Countries citing papers authored by Arnau Oliver

Since Specialization
Citations

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

Fields of papers citing papers by Arnau Oliver

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Arnau Oliver

This figure shows the co-authorship network connecting the top 25 collaborators of Arnau Oliver. A scholar is included among the top collaborators of Arnau Oliver 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 Arnau Oliver. Arnau Oliver 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.
Terceño, Mikel, et al.. (2024). Hematoma expansion prediction in intracerebral hemorrhage patients by using synthesized CT images in an end-to-end deep learning framework. Computerized Medical Imaging and Graphics. 117. 102430–102430. 7 indexed citations
2.
Oliver, Arnau, et al.. (2024). Automatic Segmentation of Sylvian Fissure in Brain Ultrasound Images of Pre-Term Infants Using Deep Learning Models. Ultrasound in Medicine & Biology. 51(3). 543–550.
3.
Oliver, Arnau, et al.. (2024). Strategies to combine 3D vasculature and brain CTA with deep neural networks: Application to LVO. iScience. 27(2). 108881–108881.
4.
Walker, Paul M., et al.. (2022). Usefulness of Collaborative Work in the Evaluation of Prostate Cancer from MRI. SHILAP Revista de lepidopterología. 12(3). 350–362. 4 indexed citations
5.
Valverde, Sergi, et al.. (2022). Minimizing the effect of white matter lesions on deep learning based tissue segmentation for brain volumetry. Computerized Medical Imaging and Graphics. 103. 102157–102157. 8 indexed citations
6.
Kushibar, Kaisar, Mostafa Salem, Sergi Valverde, et al.. (2021). Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation. Frontiers in Neuroscience. 15. 608808–608808. 7 indexed citations
7.
Cabezas, Mariano, et al.. (2020). Improving the detection of autism spectrum disorder by combining structural and functional MRI information. NeuroImage Clinical. 25. 102181–102181. 86 indexed citations
8.
Valverde, Sergi, Mostafa Salem, Mariano Cabezas, et al.. (2019). One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 104 indexed citations
9.
Valverde, Sergi, et al.. (2019). Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks. Computers in Biology and Medicine. 115. 103487–103487. 85 indexed citations
10.
Valverde, Sergi, et al.. (2018). SUNet: a deep learning architecture for acute stroke lesion segmentation and outcome prediction in multimodal MRI.. arXiv (Cornell University). 5 indexed citations
11.
Díaz, Oliver, Robert Martí, Yago Díez, et al.. (2017). Local breast density assessment using reacquired mammographic images. European Journal of Radiology. 93. 121–127. 8 indexed citations
12.
Cabezas, Mariano, Arnau Oliver, Yago Díez, et al.. (2016). Improved Automatic Detection of New T2 Lesions in Multiple Sclerosis Using Deformation Fields. American Journal of Neuroradiology. 37(10). 1816–1823. 27 indexed citations
13.
Valverde, Sergi, Arnau Oliver, Yago Díez, et al.. (2015). Evaluating the Effects of White Matter Multiple Sclerosis Lesions on the Volume Estimation of 6 Brain Tissue Segmentation Methods. American Journal of Neuroradiology. 36(6). 1109–1115. 14 indexed citations
14.
Oliver, Arnau, et al.. (2014). Stable and Critical Gesture Recognition in Children and Pregnant Women by SVM Classification with FFT Features of Signals from Wearable Attires. Research Journal of Applied Sciences Engineering and Technology. 7(23). 4917–4926. 1 indexed citations
15.
Cabezas, Mariano, Arnau Oliver, Eloy Roura, et al.. (2014). Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding. Computer Methods and Programs in Biomedicine. 115(3). 147–161. 34 indexed citations
16.
Cabezas, Mariano, Arnau Oliver, Sergi Valverde, et al.. (2014). BOOST: A supervised approach for multiple sclerosis lesion segmentation. Journal of Neuroscience Methods. 237. 108–117. 29 indexed citations
17.
Martı́, Joan, et al.. (2011). MamoDB:a web-based tool for training radiologists in the diagnosis of digital mammography. 2359–2367. 2 indexed citations
18.
Lladó, Xavier, Jordi Freixenet, & Arnau Oliver. (2010). Linking research and teaching through an applied computer vision course. International journal of engineering education. 26(6). 1445–1454. 1 indexed citations
19.
Oliver, Arnau, Jordi Freixenet, Joan Martı́, et al.. (2009). A review of automatic mass detection and segmentation in mammographic images. Medical Image Analysis. 14(2). 87–110. 278 indexed citations
20.
Oliver, Arnau, Jordi Freixenet, Joan Martı́, & M. Peracaula. (2005). Mass Segmentation using a Pattern Matching Approach with a Mutual Information Based Metric. 123–130. 2 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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