Imperial College London

Dr Ben Glocker

Faculty of EngineeringDepartment of Computing

Reader in Machine Learning for Imaging
 
 
 
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Contact

 

+44 (0)20 7594 8334b.glocker Website CV

 
 
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Location

 

377Huxley BuildingSouth Kensington Campus

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Summary

 

Summary


I am Reader in Machine Learning for Imaging co-leading the Biomedical Image Analysis Group. I lead the HeartFlow-Imperial Research Team and work as scientific adviser for Kheiron Medical Technologies.

My research is at the intersection of medical imaging and artificial intelligence aiming to build safe and ethical computational tools for improving image-based detection and diagnosis of disease.




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Selected Publications

Journal Articles

Oktay O, Nanavati J, Schwaighofer A, et al., 2020, Evaluation of deep learning to augment image-guided radiotherapy for head and neck and prostate cancers, Jama Network Open, Vol:3, ISSN:2574-3805, Pages:1-11

Coelho De Castro D, Walker I, Glocker B, 2020, Causality matters in medical imaging, Nature Communications, Vol:11, ISSN:2041-1723, Pages:1-10

Monteiro M, Newcombe VFJ, Mathieu F, et al., 2020, Multi-class semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning – an algorithm development and multi-centre validation study, The Lancet. Digital Health, Vol:2, ISSN:2589-7500, Pages:e314-e322

Lee M, Petersen K, Pawlowski N, et al., 2019, TeTrIS: template transformer networks for image segmentation with shape priors, IEEE Transactions on Medical Imaging, Vol:38, ISSN:0278-0062, Pages:2596-2606

Castro DC, Tan J, Kainz B, et al., 2019, Morpho-MNIST: quantitative assessment and diagnostics for representation learning, Journal of Machine Learning Research, Vol:20, ISSN:1532-4435, Pages:1-29

Schlemper J, Oktay O, Schaap M, et al., 2019, Attention gated networks: Learning to leverage salient regions in medical images., Med Image Anal, Vol:53, Pages:197-207

Robinson R, Valindria VV, Bai W, et al., 2019, Automated quality control in image segmentation: application to the UK Biobank cardiac MR imaging study, Journal of Cardiovascular Magnetic Resonance, Vol:21, ISSN:1097-6647

Valindria V, Lavdas I, Bai W, et al., 2017, Reverse classification accuracy: predicting segmentation performance in the absence of ground truth, IEEE Transactions on Medical Imaging, Vol:36, ISSN:1558-254X, Pages:1597-1606

Kamnitsas K, Ledig C, Newcombe VFJ, et al., 2017, Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Medical Image Analysis, Vol:36, ISSN:1361-8423, Pages:61-78

Rueckert D, Glocker B, Kainz B, 2016, Learning clinically useful information from images: Past, present and future, Medical Image Analysis, Vol:33, ISSN:1361-8423, Pages:13-18

Zikic D, Glocker B, Criminisi A, 2014, Encoding atlases by randomized classification forests for efficient multi-atlas label propagation, Medical Image Analysis, Vol:18, ISSN:1361-8423, Pages:1262-1273

Glocker B, Sotiras A, Komodakis N, et al., 2011, Deformable Medical Image Registration: Setting the State of the Art with Discrete Methods, Annual Review of Biomedical Engineering, Vol:13, ISSN:1523-9829, Pages:219-244

Glocker B, Komodakis N, Tziritas G, et al., 2008, Dense image registration through MRFs and efficient linear programming., Medical Image Analysis, Vol:12, Pages:731-741

Conference

Pawlowski N, Castro DC, Glocker B, Deep structural causal models for tractable counterfactual inference, Neural Information Processing Systems (NeurIPS), arXiv

Monteiro M, Le Folgoc L, Coelho de Castro D, et al., 2020, Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty, Curran Associates, Inc., Pages:12756-12767

More Publications