Imperial College London

Dr Ben Glocker

Faculty of EngineeringDepartment of Computing

Professor 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 Professor in Machine Learning for Imaging co-leading the Biomedical Image Analysis Group. I lead the HeartFlow-Imperial Research Team and I am also Head of ML Research at 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

Taylor-Phillips S, Seedat F, Kijauskaite G, et al., 2022, UK National Screening Committee's approach to reviewing evidence on artificial intelligence in breast cancer screening, The Lancet Digital Health, Vol:4, ISSN:2589-7500, Pages:e558-e565

Bernhardt M, Jones C, Glocker B, 2022, Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms, Nature Medicine, Vol:28, ISSN:1078-8956, Pages:1157-+

Liu X, Glocker B, McCradden MM, et al., 2022, The medical algorithmic audit., The Lancet Digital Health, Vol:4, ISSN:2589-7500, Pages:e384-e397

Bernhardt M, Castro DC, Tanno R, et al., 2022, Active label cleaning for improved dataset quality under resource constraints, Nature Communications, Vol:13

Sinclair M, Schuh A, Hahn K, et al., 2020, Atlas-ISTN: joint segmentation, registration and Atlas construction with image-and-spatial transformer networks

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

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

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