Victor Alves

5.3k total citations · 2 hit papers
57 papers, 3.0k citations indexed

About

Victor Alves is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Victor Alves has authored 57 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 14 papers in Computer Vision and Pattern Recognition and 12 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Victor Alves's work include Advanced MRI Techniques and Applications (7 papers), AI-based Problem Solving and Planning (6 papers) and Advanced Neural Network Applications (6 papers). Victor Alves is often cited by papers focused on Advanced MRI Techniques and Applications (7 papers), AI-based Problem Solving and Planning (6 papers) and Advanced Neural Network Applications (6 papers). Victor Alves collaborates with scholars based in Portugal, Austria and Germany. Victor Alves's co-authors include Carlos A. Silva, Sérgio Pereira, Adriano Pinto, Paulo Marques, Nuno Sousa, José Miguel Soares, Mauricio Reyes, Roland Wiest, Ricardo Magalhães and Richard McKinley and has published in prestigious journals such as IEEE Access, IEEE Transactions on Medical Imaging and Sensors.

In The Last Decade

Victor Alves

48 papers receiving 2.9k citations

Hit Papers

Brain Tumor Segmentation Using Convolutional Neural Netwo... 2013 2026 2017 2021 2016 2013 500 1000 1.5k
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.

  1. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
    2016 1.8k citations Sérgio Pereira, Adriano Pinto et al. profile →
  2. A hitchhiker's guide to diffusion tensor imaging
    2013 600 citations José Miguel Soares, Paulo Marques et al. Frontiers in Neuroscience profile →

Peers

Victor Alves
Carlos A. Silva Portugal
Mauricio Reyes Switzerland
Chunfeng Lian United States
U. Raghavendra India
Vishwesh Nath United States
Qingmao Hu China
Arnau Oliver Spain
Aaron Carass United States
Evangelia I. Zacharaki Greece
Jun Shi China
Carlos A. Silva Portugal
Victor Alves
Citations per year, relative to Victor Alves Victor Alves (= 1×) peers Carlos A. Silva

Countries citing papers authored by Victor Alves

Since Specialization
Citations

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

Fields of papers citing papers by Victor Alves

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Victor Alves

This figure shows the co-authorship network connecting the top 25 collaborators of Victor Alves. A scholar is included among the top collaborators of Victor Alves 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 Victor Alves. Victor Alves 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
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Puladi, Behrus, et al.. (2025). Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-Guided Radiotherapy. Lecture notes in computer science. 15273. 136–153.
3.
Alves, Victor, et al.. (2025). Unbalancing Datasets to Enhance CNN Models Learnability: A Class-Wise Metrics-Based Closed-Loop Strategy Proposal. IEEE Access. 13. 57485–57503. 1 indexed citations
4.
Carneiro, Davide, et al.. (2024). Objective metrics for ethical AI: a systematic literature review. International Journal of Data Science and Analytics. 20(2). 247–267. 10 indexed citations
5.
Puladi, Behrus, et al.. (2024). Generalisation of Segmentation Using Generative Adversarial Networks. Universitätsbibliographie, Universität Duisburg-Essen. 1–4.
6.
Adão, Telmo, et al.. (2024). Automatic Optimization of Deep Learning Training through Feature-Aware-Based Dataset Splitting. Algorithms. 17(3). 106–106. 8 indexed citations
7.
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Li, Jianning, A.J.M. Ferreira, Behrus Puladi, et al.. (2023). Open-source skull reconstruction with MONAI. SoftwareX. 23. 101432–101432. 2 indexed citations
10.
Li, Jianning, Christina Gsaxner, Antonio Pepe, et al.. (2021). Synthetic skull bone defects for automatic patient-specific craniofacial implant design. Scientific Data. 8(1). 36–36. 27 indexed citations
11.
Pinto, Adriano, Sérgio Pereira, Raphael Meier, et al.. (2020). Combining unsupervised and supervised learning for predicting the final stroke lesion. Medical Image Analysis. 69. 101888–101888. 25 indexed citations
12.
Lori, Nicolás, et al.. (2018). Deep Learning Based Pipeline for Fingerprinting Using Brain Functional MRI Connectivity Data. Procedia Computer Science. 141. 539–544. 8 indexed citations
13.
Coelho, Ana, Paulo Marques, Ricardo Magalhães, et al.. (2017). A Knowledge Representation and Reasoning System for Multimodal Neuroimaging Studies. INTELIGENCIA ARTIFICIAL. 20(59). 42–42. 2 indexed citations
14.
Pereira, Sérgio, Raphael Meier, Richard McKinley, et al.. (2017). Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation. Medical Image Analysis. 44. 228–244. 79 indexed citations
15.
Soares, José Miguel, Ricardo Magalhães, Pedro Silva Moreira, et al.. (2016). A Hitchhiker's Guide to Functional Magnetic Resonance Imaging. Frontiers in Neuroscience. 10. 515–515. 167 indexed citations
16.
Lori, Nicolás, Agustín Ibáñez, Rui D. M. Travasso, et al.. (2016). Processing Time Reduction: an Application in Living Human High-Resolution Diffusion Magnetic Resonance Imaging Data. Journal of Medical Systems. 40(11). 243–243. 2 indexed citations
17.
Magalhães, Ricardo, Paulo Marques, José Miguel Soares, Victor Alves, & Nuno Sousa. (2014). The Impact of Normalization and Segmentation on Resting-State Brain Networks. Brain Connectivity. 5(3). 166–176. 14 indexed citations
18.
Neves, José, et al.. (2011). A logic programming approach to medical errors in imaging. International Journal of Medical Informatics. 80(9). 669–679. 7 indexed citations
19.
Silva, Ana, Noeme Sousa Rocha, Victor Alves, et al.. (2004). Tremor as the first neurological manifestation of Sneddon's syndrome. Movement Disorders. 20(2). 248–251. 7 indexed citations
20.
Santos, Manuel Filipe, José Neves, & Victor Alves. (2002). The Inventive Power Of Learning Classifier Systems: A Contribution To Data Mining. WIT transactions on information and communication technologies. 28.

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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