M. Jorge Cardoso

33.3k total citations · 5 hit papers
253 papers, 10.2k citations indexed

About

M. Jorge Cardoso is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, M. Jorge Cardoso has authored 253 papers receiving a total of 10.2k indexed citations (citations by other indexed papers that have themselves been cited), including 103 papers in Radiology, Nuclear Medicine and Imaging, 53 papers in Computer Vision and Pattern Recognition and 42 papers in Artificial Intelligence. Recurrent topics in M. Jorge Cardoso's work include Medical Image Segmentation Techniques (44 papers), Advanced Neuroimaging Techniques and Applications (43 papers) and Medical Imaging Techniques and Applications (32 papers). M. Jorge Cardoso is often cited by papers focused on Medical Image Segmentation Techniques (44 papers), Advanced Neuroimaging Techniques and Applications (43 papers) and Medical Imaging Techniques and Applications (32 papers). M. Jorge Cardoso collaborates with scholars based in United Kingdom, United States and Netherlands. M. Jorge Cardoso's co-authors include Sébastien Ourselin, Carole H. Sudre, Wenqi Li, Tom Vercauteren, Marc Modat, Tal Arbel, David M. Cash, Nick C. Fox, Josephine Barnes and Jonathan M. Schott and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Medicine and Nature Communications.

In The Last Decade

M. Jorge Cardoso

246 papers receiving 10.0k citations

Hit Papers

5 papers align trajectories

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.

Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations

2017 1.5k citations Carole H. Sudre, Wenqi Li et al. Lecture notes in computer science profile →
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

2017 970 citations M. Jorge Cardoso, Tal Arbel et al. Lecture notes in computer science profile →
Real-time tracking of self-reported symptoms to predict potential COVID-19

2020 847 citations Carole H. Sudre, Thomas Varsavsky et al. profile →
NiftyNet: a deep-learning platform for medical imaging

2018 363 citations Eli Gibson, Wenqi Li et al. Computer Methods and Programs in Biomedicine profile →
Serum neurofilament light chain protein is a measure of disease intensity in frontotemporal dementia

2016 319 citations Jonathan D. Rohrer, M. Jorge Cardoso et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
M. Jorge Cardoso United Kingdom	44	3.5k	2.2k	1.8k	1.2k	1.2k	253	10.2k
Dzung L. Pham United States	44	3.2k 0.9×	3.5k 1.6×	1.0k 0.6×	1.2k 1.0×	576 0.5×	194	9.8k
Bennett A. Landman United States	49	6.2k 1.8×	2.7k 1.2×	1.6k 0.9×	726 0.6×	808 0.7×	465	11.0k
Marc Modat United Kingdom	44	3.4k 1.0×	1.5k 0.7×	649 0.4×	701 0.6×	1.3k 1.1×	178	7.1k
J. M. Górriz Spain	53	1.9k 0.5×	2.4k 1.1×	2.6k 1.4×	570 0.5×	950 0.8×	297	9.0k
Feng Shi China	61	4.2k 1.2×	1.8k 0.8×	1.5k 0.9×	408 0.3×	902 0.8×	448	12.0k
Michael Fulham Australia	48	3.7k 1.0×	1.7k 0.8×	1.8k 1.0×	470 0.4×	489 0.4×	269	9.0k
Yuanjie Zheng China	34	3.6k 1.0×	2.4k 1.1×	1.6k 0.9×	341 0.3×	412 0.4×	244	8.9k
Ayman El‐Baz United States	52	4.3k 1.2×	1.8k 0.9×	1.9k 1.0×	413 0.3×	658 0.6×	665	11.4k
Simon K. Warfield United States	68	7.8k 2.2×	4.6k 2.1×	1.6k 0.9×	740 0.6×	1.1k 0.9×	449	21.5k
Jean‐Philippe Thiran Switzerland	60	7.1k 2.0×	3.2k 1.5×	1.2k 0.7×	1.7k 1.4×	716 0.6×	563	16.7k

Countries citing papers authored by M. Jorge Cardoso

Since Specialization

Citations

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

Fields of papers citing papers by M. Jorge Cardoso

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Jorge Cardoso

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Fernandez, Virginia, Walter Hugo Lopez Pinaya, Mark S. Graham, et al.. (2024). Generating multi-pathological and multi-modal images and labels for brain MRI. Medical Image Analysis. 97. 103278–103278. 2 indexed citations

Murray, Benjamin, Richard Brown, Eric Kerfoot, et al.. (2024). Lazy Resampling: Fast and information preserving preprocessing for deep learning. Computer Methods and Programs in Biomedicine. 257. 108422–108422. 1 indexed citations

Nderitu, Paul, Samantha Mann, M. Jorge Cardoso, et al.. (2024). Predicting 1, 2 and 3 year emergent referable diabetic retinopathy and maculopathy using deep learning. SHILAP Revista de lepidopterología. 4(1). 167–167. 2 indexed citations

Camargo, Raphael Y. de, Sylvain Chevallier, Adam G. Thomas, et al.. (2023). Synthetic Sleep EEG Signal Generation using Latent Diffusion Models. SPIRE - Sciences Po Institutional REpository. 3 indexed citations

Kläser, Kerstin, Marc Modat, David Atkinson, et al.. (2021). A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis. Applied Sciences. 11(4). 1667–1667. 10 indexed citations

Markiewicz, Paweł, Julian C. Matthews, John Ashburner, et al.. (2021). Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging. NeuroImage. 232. 117821–117821. 10 indexed citations

Convery, Rhian S., Mollie Neason, David M. Cash, et al.. (2020). Basal forebrain atrophy in frontotemporal dementia. NeuroImage Clinical. 26. 102210–102210. 12 indexed citations

Ma, Da, M. Jorge Cardoso, María A. Zuluaga, et al.. (2020). Substantially thinner internal granular layer and reduced molecular layer surface in the cerebellar cortex of the Tc1 mouse model of down syndrome – a comprehensive morphometric analysis with active staining contrast-enhanced MRI. NeuroImage. 223. 117271–117271. 4 indexed citations

Sudre, Carole H., et al.. (2020). A k-Space Model of Movement Artefacts: Application to Segmentation Augmentation and Artefact Removal. IEEE Transactions on Medical Imaging. 39(9). 2881–2892. 24 indexed citations

10.

Eshaghi, Arman, Rogier Kievit, Ferrán Prados, et al.. (2019). Applying causal models to explore the mechanism of action of simvastatin in progressive multiple sclerosis. Proceedings of the National Academy of Sciences. 116(22). 11020–11027. 20 indexed citations

11.

Wang, Qian, Fausto Milletarì, Hien Van Nguyen, et al.. (2019). Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings. 4 indexed citations

12.

Canas, Liane S., Carole H. Sudre, Enrico De Vita, et al.. (2019). Prion disease diagnosis using subject-specific imaging biomarkers within a multi-kernel Gaussian process. NeuroImage Clinical. 24. 102051–102051. 4 indexed citations

13.

Gibson, Eli, Wenqi Li, Carole H. Sudre, et al.. (2018). NiftyNet: a deep-learning platform for medical imaging. Computer Methods and Programs in Biomedicine. 158. 113–122. 363 indexed citations breakdown →

14.

Müller, Henning, B. Michael Kelm, Tal Arbel, et al.. (2017). Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. Lecture notes in computer science. 19 indexed citations

15.

Andrews, Katharine, Chris Frost, Marc Modat, et al.. (2015). Acceleration of hippocampal atrophy rates in asymptomatic amyloidosis. Neurobiology of Aging. 39. 99–107. 18 indexed citations

16.

Cardoso, M. Jorge, Kelvin K. Leung, Marc Modat, et al.. (2013). STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation. Medical Image Analysis. 17(6). 671–684. 170 indexed citations

17.

Cardoso, M. Jorge. (2008). Developing Dynamic Packaging Systems using Semantic Web Technologies. 5 indexed citations

18.

Cardoso, M. Jorge, José Antônio Cordeiro, & Joaquim Filipe. (2007). ICEIS 2007 - Proceedings of the Ninth International Conference on Enterprise Information Systems, Volume AIDSS, Funchal, Madeira, Portugal, June 12-16, 2007. International Conference on Enterprise Information Systems. 1 indexed citations

19.

Lima, Carlos & M. Jorge Cardoso. (2007). Phonocardiogram segmentation by using an hybrid RBF-HMM model. RepositóriUM (Universidade do Minho). 419–422. 2 indexed citations

20.

Cardoso, M. Jorge. (2005). About the Data-Flow Complexity of Web Processes. 10 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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