Ben Glocker
Impact in
- Health Informatics top 0.01%
- Artificial Intelligence in Healthcare and Education
- Neurology top 0.05%
- Brain Tumor Detection and Classification
- Traumatic Brain Injury and Neurovascular Disturbances
Papers in
-
- Medical Image Segmentation Techniques 150
-
- Artificial Intelligence in Healthcare and Education 27
- Co-authors
- Daniel Rueckert (60 shared papers)Konstantinos Kamnitsas (30 shared papers)Antonio Criminisi (25 shared papers)Christian Ledig (8 shared papers)Virginia Newcombe (12 shared papers)David Krishna Menon (11 shared papers)Andrew D. Kane (2 shared papers)Ozan Oktay (16 shared papers)
- Journals
- Medical Image Analysis (23 papers)Journal of Neurotrauma (18 papers)IEEE Transactions on Medical Imaging (16 papers)Lecture notes in computer science (98 papers)The Lancet Neurology (6 papers)
- Partner nations
- United KingdomGermanyUnited States
In The Last Decade
Ben Glocker
319 papers receiving 29.4k citations
Ben Glocker's Hit Papers
Peers
Comparison fields: 5 of 193
- Health Informatics 2.4k
- Neurology 4.5k
- Computer Vision and Pattern Recognition 10.8k
- Neurology 2.4k
- Radiology, Nuclear Medicine and Imaging 3k
Countries citing papers authored by Ben Glocker
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
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-authors
The 25 scholars most cited alongside Ben Glocker, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 336 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) Hit paper breakdown → | 2014 | 4968 |
| 2 | Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Hit paper breakdown → | 2016 | 2720 |
| 3 | Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research Hit paper breakdown → | 2017 | 1951 |
| 4 | Attention gated networks: Learning to leverage salient regions in medical images Hit paper breakdown → | 2019 | 1494 |
| 5 | TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods Hit paper breakdown → | 2024 | 1335 |
| 6 | Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images Hit paper breakdown → | 2013 | 725 |
| 7 | ElasticFusion: Dense SLAM Without A Pose Graph Hit paper breakdown → | 2015 | 623 |
| 8 | Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation Hit paper breakdown → | 2017 | 579 |
| 9 | Automated cardiovascular magnetic resonance image analysis with fully convolutional networks Hit paper breakdown → | 2018 | 578 |
| 10 | Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease Hit paper breakdown → | 2018 | 557 |
| 11 | ElasticFusion: Real-time dense SLAM and light source estimation Hit paper breakdown → | 2016 | 547 |
| 12 | ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI Hit paper breakdown → | 2016 | 425 |
| 13 | Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI Hit paper breakdown → | 2022 | 410 |
| 14 | Case-mix, care pathways, and outcomes in patients with traumatic brain injury in CENTER-TBI: a European prospective, multicentre, longitudinal, cohort study Hit paper breakdown → | 2019 | 375 |
| 15 | Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge Hit paper breakdown → | 2011 | 371 |
| 16 | Unsupervised Domain Adaptation in Brain Lesion Segmentation with Adversarial Networks Hit paper breakdown → | 2017 | 363 |
| 17 | 2018 | 350 | |
| 18 | 2008 | 343 | |
| 19 | 2017 | 327 | |
| 20 | 2017 | 295 |
About Ben Glocker
Ben Glocker is a scholar working on Computer Vision and Pattern Recognition, Health Informatics, Neurology, Artificial Intelligence and Epidemiology, having authored 336 papers that have together received 30.1k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (150 papers), Traumatic Brain Injury Research (42 papers), Traumatic Brain Injury and Neurovascular Disturbances (38 papers), Artificial Intelligence in Healthcare and Education (27 papers), Medical Imaging and Analysis (17 papers), Brain Tumor Detection and Classification (17 papers), AI in cancer detection (16 papers) and Domain Adaptation and Few-Shot Learning (15 papers). The work is most often cited by research in Health Informatics (2.4k citations), Neurology (4.5k citations), Computer Vision and Pattern Recognition (10.8k citations), Neurology (2.4k citations) and Radiology, Nuclear Medicine and Imaging (3k citations). Ben Glocker has collaborated with scholars based in United Kingdom, Germany and United States. Frequent co-authors include Daniel Rueckert, Konstantinos Kamnitsas, Antonio Criminisi, Christian Ledig, Virginia Newcombe, David Krishna Menon, Andrew D. Kane, Ozan Oktay, Bernhard Kainz and Enzo Ferrante. Their work appears in journals such as Medical Image Analysis, Journal of Neurotrauma, IEEE Transactions on Medical Imaging, Lecture notes in computer science and The Lancet Neurology.
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.