Ben Glocker

43.7k total citations · 7 hit papers
151 papers, 10.1k citations indexed

About

Ben Glocker is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Ben Glocker has authored 151 papers receiving a total of 10.1k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Computer Vision and Pattern Recognition, 50 papers in Radiology, Nuclear Medicine and Imaging and 48 papers in Artificial Intelligence. Recurrent topics in Ben Glocker's work include Medical Image Segmentation Techniques (43 papers), Radiomics and Machine Learning in Medical Imaging (32 papers) and AI in cancer detection (22 papers). Ben Glocker is often cited by papers focused on Medical Image Segmentation Techniques (43 papers), Radiomics and Machine Learning in Medical Imaging (32 papers) and AI in cancer detection (22 papers). Ben Glocker collaborates with scholars based in United Kingdom, United States and Germany. Ben Glocker's co-authors include Daniel Rueckert, Konstantinos Kamnitsas, Christian Ledig, David Menon, Virginia Newcombe, Antonio Criminisi, Andrew D. Kane, Joanna Simpson, Ozan Oktay and Bernhard Kainz and has published in prestigious journals such as Nature Medicine, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Ben Glocker

147 papers receiving 9.8k citations

Hit Papers

Efficient multi-scale 3D CNN with fully connected CRF for... 2013 2026 2017 2021 2016 2019 2013 2015 2016 500 1000 1.5k 2.0k
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. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
    2016 2.1k citations Konstantinos Kamnitsas, Virginia Newcombe et al. Medical Image Analysis profile →
  2. Attention gated networks: Learning to leverage salient regions in medical images
    2019 1.2k citations Ozan Oktay, Michiel Schaap et al. Medical Image Analysis profile →
  3. Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images
    2013 546 citations Jamie Shotton, Ben Glocker et al. profile →
  4. ElasticFusion: Dense SLAM Without A Pose Graph
    2015 497 citations Ben Glocker et al. profile →
  5. ElasticFusion: Real-time dense SLAM and light source estimation
    2016 457 citations Ben Glocker et al. profile →
  6. Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
    2017 438 citations Ozan Oktay, Enzo Ferrante et al. IEEE Transactions on Medical Imaging profile →
  7. Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease
    2018 432 citations Enzo Ferrante, Ben Glocker et al. Medical Image Analysis profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ben Glocker United Kingdom 40 5.0k 3.4k 2.2k 1.6k 1.5k 151 10.1k
Evan Shelhamer United States 8 10.7k 2.1× 2.2k 0.6× 4.4k 2.0× 1.1k 0.7× 598 0.4× 12 17.3k
Jonathan Long United States 6 9.9k 2.0× 2.1k 0.6× 4.1k 1.8× 958 0.6× 583 0.4× 8 16.3k
Liang-Chieh Chen United States 15 12.8k 2.5× 2.2k 0.6× 5.3k 2.4× 1.3k 0.8× 569 0.4× 25 19.0k
Dirk Vandermeulen Belgium 44 5.7k 1.1× 3.7k 1.1× 1.0k 0.5× 835 0.5× 669 0.4× 200 11.0k
Dmitry B. Goldgof United States 46 3.3k 0.7× 4.1k 1.2× 2.4k 1.1× 630 0.4× 756 0.5× 275 9.5k
Iasonas Kokkinos France 19 11.0k 2.2× 2.0k 0.6× 4.7k 2.1× 1.1k 0.7× 526 0.3× 42 16.7k
Nicholas Ayache France 77 11.6k 2.3× 8.0k 2.4× 2.2k 1.0× 2.0k 1.2× 1.0k 0.7× 447 23.2k
Pheng‐Ann Heng Hong Kong 71 11.5k 2.3× 6.8k 2.0× 7.1k 3.2× 533 0.3× 1.5k 1.0× 535 22.8k
William M. Wells United States 48 6.3k 1.2× 5.9k 1.7× 1.9k 0.9× 570 0.3× 1.0k 0.7× 209 14.7k
Nikos Paragios France 46 6.2k 1.2× 3.1k 0.9× 1.4k 0.6× 870 0.5× 300 0.2× 198 10.5k

Countries citing papers authored by Ben Glocker

Since Specialization
Citations

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

Fields of papers citing papers by Ben Glocker

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ben Glocker

This figure shows the co-authorship network connecting the top 25 collaborators of Ben Glocker. A scholar is included among the top collaborators of Ben Glocker 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 Ben Glocker. Ben Glocker 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.
Sinclair, Matthew, et al.. (2024). Image-To-Tree with Recursive Prompting. 1–5. 2 indexed citations
2.
Kale, Aditya U., Henry David Jeffry Hogg, Ben Glocker, et al.. (2024). AI as a Medical Device Adverse Event Reporting in Regulatory Databases: Protocol for a Systematic Review. JMIR Research Protocols. 13. e48156–e48156. 1 indexed citations
3.
Trivedi, Hari, et al.. (2024). Generalisable deep learning method for mammographic density prediction across imaging techniques and self-reported race. SHILAP Revista de lepidopterología. 4(1). 21–21. 1 indexed citations
4.
Winkler, Mathias, et al.. (2024). Robust prostate disease classification using transformers with discrete representations. International Journal of Computer Assisted Radiology and Surgery. 20(1). 11–20. 1 indexed citations
5.
Winzeck, Stefan, Evgenios Kornaropoulos, Sophie Richter, et al.. (2023). Use of Support Vector Machines Approach via ComBat Harmonized Diffusion Tensor Imaging for the Diagnosis and Prognosis of Mild Traumatic Brain Injury: A CENTER-TBI Study. Journal of Neurotrauma. 40(13-14). 1317–1338. 10 indexed citations
6.
7.
Ng, Annie, Ben Glocker, Cary Oberije, et al.. (2023). Artificial Intelligence as Supporting Reader in Breast Screening: A Novel Workflow to Preserve Quality and Reduce Workload. Journal of Breast Imaging. 5(3). 267–276. 17 indexed citations
8.
9.
Sinclair, Matthew, Andreas Schuh, Kersten Petersen, et al.. (2022). Atlas-ISTN: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks. Medical Image Analysis. 78. 102383–102383. 18 indexed citations
10.
11.
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
12.
Whitehouse, Daniel, Miguel Monteiro, Endre Czeiter, et al.. (2021). Relationship of admission blood proteomic biomarkers levels to lesion type and lesion burden in traumatic brain injury: A CENTER-TBI study. EBioMedicine. 75. 103777–103777. 30 indexed citations
13.
Pawlowski, Nick, Daniel C. Castro, & Ben Glocker. (2020). Deep Structural Causal Models for Tractable Counterfactual Inference. Spiral (Imperial College London). 33. 857–869. 3 indexed citations
14.
Tarroni, Giacomo, Wenjia Bai, Ozan Oktay, et al.. (2020). Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank. Scientific Reports. 10(1). 2408–2408. 16 indexed citations
15.
Muñoz-González, Luis, et al.. (2019). Universal Adversarial Perturbations to Understand Robustness of Texture vs. Shape-biased Training. arXiv (Cornell University). 1 indexed citations
16.
Winzeck, Stefan, Steven J. T. Mocking, Mark J.R.J. Bouts, et al.. (2019). Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI. American Journal of Neuroradiology. 40(6). 938–945. 38 indexed citations
17.
Tarroni, Giacomo, Ozan Oktay, Wenjia Bai, et al.. (2018). Learning-Based Quality Control for Cardiac MR Images. IEEE Transactions on Medical Imaging. 38(5). 1127–1138. 37 indexed citations
18.
Oktay, Ozan, Enzo Ferrante, Konstantinos Kamnitsas, et al.. (2017). Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation. IEEE Transactions on Medical Imaging. 37(2). 384–395. 438 indexed citations breakdown →
19.
Glocker, Ben, Darko Zikic, Ender Konukoğlu, David R. Haynor, & Antonio Criminisi. (2013). Vertebrae Localization in Pathological Spine CT via Dense Classification from Sparse Annotations. Lecture notes in computer science. 16(Pt 2). 262–270. 101 indexed citations
20.
Zikic, Darko, Ben Glocker, Ender Konukoğlu, et al.. (2012). Context-sensitive Classication Forests for Segmentation of Brain Tumor Tissues. Biochemical Pharmacology. 201. 115075–115075. 42 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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