Kaisar Kushibar

1.3k total citations
16 papers, 497 citations indexed

About

Kaisar Kushibar is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Kaisar Kushibar has authored 16 papers receiving a total of 497 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 7 papers in Radiology, Nuclear Medicine and Imaging and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Kaisar Kushibar's work include AI in cancer detection (8 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Brain Tumor Detection and Classification (5 papers). Kaisar Kushibar is often cited by papers focused on AI in cancer detection (8 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Brain Tumor Detection and Classification (5 papers). Kaisar Kushibar collaborates with scholars based in Spain, United States and Sweden. Kaisar Kushibar's co-authors include Arnau Oliver, Xavier Lladó, Mariano Cabezas, Karim Lekadir, Akis Linardos, Sergi Valverde, José Bernal, Polyxeni Gkontra, Oliver Díaz and Richard Osuala and has published in prestigious journals such as Scientific Reports, IEEE Access and Frontiers in Neuroscience.

In The Last Decade

Kaisar Kushibar

15 papers receiving 483 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kaisar Kushibar Spain 11 210 200 143 89 89 16 497
Fernando Pérez‐García United Kingdom 9 141 0.7× 226 1.1× 107 0.7× 48 0.5× 41 0.5× 17 503
April Khademi Canada 14 134 0.6× 232 1.2× 173 1.2× 60 0.7× 99 1.1× 48 525
Mukti E. Jadhav India 11 256 1.2× 146 0.7× 132 0.9× 59 0.7× 126 1.4× 26 653
Michael Kawczynski United States 5 142 0.7× 232 1.2× 51 0.4× 38 0.4× 111 1.2× 10 486
Felix Achilles Germany 5 312 1.5× 181 0.9× 169 1.2× 90 1.0× 50 0.6× 9 530
Polyxeni Gkontra Spain 14 157 0.7× 240 1.2× 63 0.4× 22 0.2× 40 0.4× 31 616
Yunzhi Huang China 8 136 0.6× 150 0.8× 89 0.6× 30 0.3× 27 0.3× 20 292
Donghuan Lu China 11 216 1.0× 329 1.6× 262 1.8× 55 0.6× 163 1.8× 29 686
D. Rodríguez United Kingdom 11 73 0.3× 157 0.8× 53 0.4× 109 1.2× 34 0.4× 23 367
Hongming Xu China 15 266 1.3× 182 0.9× 138 1.0× 31 0.3× 24 0.3× 46 575

Countries citing papers authored by Kaisar Kushibar

Since Specialization
Citations

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

Fields of papers citing papers by Kaisar Kushibar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kaisar Kushibar

This figure shows the co-authorship network connecting the top 25 collaborators of Kaisar Kushibar. A scholar is included among the top collaborators of Kaisar Kushibar 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 Kaisar Kushibar. Kaisar Kushibar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Joshi, Saurabh, Richard Osuala, Javier del Riego, et al.. (2024). Leveraging epistemic uncertainty to improve tumour segmentation in breast MRI: an exploratory analysis. 39–39.
2.
Kushibar, Kaisar, Richard Osuala, Oliver Díaz, et al.. (2023). High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection. Frontiers in Oncology. 12. 1044496–1044496. 16 indexed citations
3.
Osuala, Richard, et al.. (2023). medigan: a Python library of pretrained generative models for medical image synthesis. Journal of Medical Imaging. 10(6). 61403–61403. 14 indexed citations
4.
Osuala, Richard, Kaisar Kushibar, Akis Linardos, et al.. (2022). Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging. Medical Image Analysis. 84. 102704–102704. 41 indexed citations
5.
Linardos, Akis, Kaisar Kushibar, Seán Walsh, Polyxeni Gkontra, & Karim Lekadir. (2022). Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease. Scientific Reports. 12(1). 3551–3551. 66 indexed citations
6.
Kushibar, Kaisar, et al.. (2022). Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study. Artificial Intelligence in Medicine. 132. 102386–102386. 24 indexed citations
7.
Osuala, Richard, et al.. (2022). Sharing generative models instead of private data: a simulation study on mammography patch classification. Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona). 45–45. 1 indexed citations
8.
Osuala, Richard, Kaisar Kushibar, Akis Linardos, et al.. (2021). A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions.. Spiral (Imperial College London). 8 indexed citations
9.
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
10.
Díaz, Oliver, Kaisar Kushibar, Richard Osuala, et al.. (2021). Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools. Physica Medica. 83. 25–37. 78 indexed citations
11.
Osuala, Richard, Kaisar Kushibar, Akis Linardos, et al.. (2021). Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging. arXiv (Cornell University). 1 indexed citations
12.
Bernal, José, Sergi Valverde, Kaisar Kushibar, et al.. (2021). Generating Longitudinal Atrophy Evaluation Datasets on Brain Magnetic Resonance Images Using Convolutional Neural Networks and Segmentation Priors. Neuroinformatics. 19(3). 477–492. 12 indexed citations
13.
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
14.
Kushibar, Kaisar, Sergi Valverde, Sandra González-Villà, et al.. (2019). Supervised Domain Adaptation for Automatic Sub-cortical Brain Structure Segmentation with Minimal User Interaction. Scientific Reports. 9(1). 6742–6742. 29 indexed citations
15.
Bernal, José, Kaisar Kushibar, Mariano Cabezas, et al.. (2019). Quantitative Analysis of Patch-Based Fully Convolutional Neural Networks for Tissue Segmentation on Brain Magnetic Resonance Imaging. IEEE Access. 7. 89986–90002. 30 indexed citations
16.
Kushibar, Kaisar, Sergi Valverde, Sandra González-Villà, et al.. (2018). Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features. Medical Image Analysis. 48. 177–186. 84 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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2026