J. M. Górriz

14.5k total citations · 5 hit papers
297 papers, 9.0k citations indexed

About

J. M. Górriz is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Neurology. According to data from OpenAlex, J. M. Górriz has authored 297 papers receiving a total of 9.0k indexed citations (citations by other indexed papers that have themselves been cited), including 106 papers in Computer Vision and Pattern Recognition, 91 papers in Artificial Intelligence and 72 papers in Neurology. Recurrent topics in J. M. Górriz's work include Brain Tumor Detection and Classification (66 papers), Medical Image Segmentation Techniques (53 papers) and Blind Source Separation Techniques (37 papers). J. M. Górriz is often cited by papers focused on Brain Tumor Detection and Classification (66 papers), Medical Image Segmentation Techniques (53 papers) and Blind Source Separation Techniques (37 papers). J. M. Górriz collaborates with scholars based in Spain, United Kingdom and Germany. J. M. Górriz's co-authors include Javier Ramı́rez, D. Salas-González, Yudong Zhang, I. Álvarez, Andrés Ortíz, F. Segovia, Shuihua Wang‎, Carlos G. Puntonet, Francisco J. Martínez-Murcia and M. López and has published in prestigious journals such as Journal of Clinical Oncology, Journal of the American College of Cardiology and PLoS ONE.

In The Last Decade

J. M. Górriz

288 papers receiving 8.6k citations

Hit Papers

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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.

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

2020 313 citations Yudong Zhang, Shuihua Wang‎ et al. Information Fusion profile →
Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network

2020 277 citations Shuihua Wang‎, J. M. Górriz et al. Information Fusion profile →
Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network

2020 260 citations Yudong Zhang, David S. Guttery et al. profile →
Deep learning in food category recognition

2023 245 citations Yudong Zhang, J. M. Górriz et al. Information Fusion profile →
Emotion recognition in EEG signals using deep learning methods: A review

2023 107 citations Mahboobeh Jafari, Afshin Shoeibi et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
J. M. Górriz	2.6k	2.4k	2.0k	1.9k	1.7k	297	9.0k
Heung‐Il Suk	2.7k 1.1×	2.0k 0.9×	1.6k 0.8×	2.6k 1.4×	2.0k 1.2×	136	8.4k
Javier Ramı́rez	2.0k 0.8×	1.7k 0.7×	1.6k 0.8×	905 0.5×	1.1k 0.6×	263	6.6k
Daoqiang Zhang	3.7k 1.4×	4.5k 1.9×	2.2k 1.1×	2.2k 1.2×	2.9k 1.7×	368	12.7k
Baiying Lei	3.1k 1.2×	2.6k 1.1×	1.0k 0.5×	2.4k 1.3×	844 0.5×	296	8.1k
Shuihua Wang‎	6.0k 2.3×	5.2k 2.2×	4.3k 2.1×	3.4k 1.8×	1.0k 0.6×	356	16.5k
Jasjit S. Suri	2.2k 0.9×	2.4k 1.0×	1.0k 0.5×	4.8k 2.5×	2.0k 1.1×	436	15.2k
Seong‐Whan Lee	3.0k 1.2×	4.8k 2.0×	1.0k 0.5×	1.1k 0.6×	6.2k 3.6×	508	14.3k
Xiaofeng Zhu	4.4k 1.7×	4.7k 1.9×	660 0.3×	1.1k 0.6×	655 0.4×	239	10.1k
Hamid Soltanian‐Zadeh	1.3k 0.5×	2.4k 1.0×	818 0.4×	2.3k 1.2×	1.4k 0.8×	456	7.3k
Pew‐Thian Yap	1.5k 0.6×	3.2k 1.3×	890 0.4×	3.2k 1.7×	2.2k 1.3×	305	8.7k

Countries citing papers authored by J. M. Górriz

Since Specialization

Citations

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

Fields of papers citing papers by J. M. Górriz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J. M. Górriz. 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 J. M. Górriz. The network helps show where J. M. Górriz may publish in the future.

Co-authorship network of co-authors of J. M. Górriz

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

All Works

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20 of 20 papers shown

Khan, Muhammad Attique, et al.. (2025). Skin cancer segmentation and recognition from dermoscopy images: a novel framework based on improved DeepLabV3+ and network-level fused deep architectures. Journal of Advanced Research.

Wagemann, Olivia, Francisco J. Martínez-Murcia, Elisabeth Wlasich, et al.. (2025). Exploratory analysis of the proteomic profile in plasma in adults with Down syndrome in the context of Alzheimer's disease. Alzheimer s & Dementia. 21(3). e70040–e70040. 5 indexed citations

Elazab, Ahmed, Changmiao Wang, M. Abdel-Aziz, et al.. (2024). Alzheimer’s disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions. Expert Systems with Applications. 255. 124780–124780. 39 indexed citations

Martínez-Murcia, Francisco J., Juan E. Arco, C. Jiménez-Mesa, et al.. (2024). Bridging Imaging and Clinical Scores in Parkinson’s Progression via Multimodal Self-Supervised Deep Learning. International Journal of Neural Systems. 34(8). 2450043–2450043. 7 indexed citations

Damaševičius, Robertas, Senthil Kumar Jagatheesaperumal, Kandala N. V. P. S. Rajesh, et al.. (2024). Deep learning for personalized health monitoring and prediction: A review. Computational Intelligence. 40(3). 11 indexed citations

Alizadehsani, Roohallah, et al.. (2024). Artificial Intelligence in Eye Movements Analysis for Alzheimer’s Disease Early Diagnosis. Current Alzheimer Research. 21(3). 155–165. 6 indexed citations

Попов, Антон, et al.. (2024). Improved organs at risk segmentation based on modified U‐Net with self‐attention and consistency regularisation. CAAI Transactions on Intelligence Technology. 9(4). 850–865. 1 indexed citations

Roshanzamir, Mohamad, Mahboobeh Jafari, Roohallah Alizadehsani, et al.. (2024). What Happens in Face During a Facial Expression? Using Data Mining Techniques to Analyze Facial Expression Motion Vectors. Information Systems Frontiers. 5 indexed citations

Ghassemi, Navid, Afshin Shoeibi, Marjane Khodatars, et al.. (2023). Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning. Applied Soft Computing. 144. 110511–110511. 35 indexed citations

10.

Castillo-Barnés, Diego, Francisco J. Martínez-Murcia, C. Jiménez-Mesa, et al.. (2023). Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson’s Disease Using Multimodal Data. International Journal of Neural Systems. 33(8). 2350041–2350041. 11 indexed citations

11.

Arco, Juan E., et al.. (2023). Enhancing Multimodal Patterns in Neuroimaging by Siamese Neural Networks with Self-Attention Mechanism. International Journal of Neural Systems. 33(4). 2350019–2350019. 13 indexed citations

12.

Yu, Xiang, Shuihua Wang‎, J. M. Górriz, et al.. (2022). PeMNet for Pectoral Muscle Segmentation. Biology. 11(1). 134–134. 8 indexed citations

13.

Górriz, J. M., C. Jiménez-Mesa, F. Segovia, et al.. (2021). Statistical Agnostic Mapping: A framework in neuroimaging based on concentration inequalities. arXiv (Cornell University). 13 indexed citations

14.

Romero-García, Rafael, Michael G. Hart, Richard A. I. Bethlehem, et al.. (2021). BOLD Coupling between Lesioned and Healthy Brain Is Associated with Glioma Patients’ Recovery. Cancers. 13(19). 5008–5008. 8 indexed citations

15.

Castillo-Barnés, Diego, Li Su, Javier Ramı́rez, et al.. (2020). Autosomal dominantly inherited alzheimer disease: Analysis of genetic subgroups by machine learning. Information Fusion. 58. 153–167. 15 indexed citations

16.

Martínez-Murcia, Francisco J., Andrés Ortíz, J. M. Górriz, Javier Ramı́rez, & Diego Castillo-Barnés. (2019). Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders. IEEE Journal of Biomedical and Health Informatics. 24(1). 17–26. 154 indexed citations

17.

Álvarez, I., Javier Ramı́rez, J. M. Górriz, et al.. (2018). Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Contrast Media & Molecular Imaging. 2018. 1–11. 14 indexed citations

18.

Segovia, F., I. Álvarez, D. Salas-González, et al.. (2014). PETRA: Multivariate analyses for neuroimaging data. Open Repository and Bibliography (University of Liège). 1302–1312. 2 indexed citations

19.

Segovia, F., et al.. (2012). Automatic differentiation between controls and Parkinson's disease DaTSCAN images using a Partial Least Squares scheme and the Fisher Discriminant Ratio.. 2241–2250. 3 indexed citations

20.

Górriz, J. M., Carlos G. Puntonet, & Elmar W. Lang. (2004). Hybrid ICA - ANN model applied to volatile time series forecasting. University of Regensburg Publication Server (University of Regensburg). 1 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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