Ben Glocker

Ben Glocker is Professor in Machine Learning for Imaging at the Department of Computing at Imperial College London where he co-leads the Biomedical Image Analysis Group. He is the Kheiron Medical Technologies / Royal Academy of Engineering Research Chair in Safe Deployment of Medical Imaging AI. He also leads the HeartFlow-Imperial Research Team and is Head of ML Research at Kheiron. He holds a PhD from TU Munich and was a postdoc at Microsoft and a Research Fellow at the University of Cambridge. His 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. He has received several awards including a Philips Impact Award, a Medical Image Analysis – MICCAI Best Paper Award, and the Francois Erbsmann Prize. He is a member of the Young Scientists Community of the World Economic Forum and a member of the AI Task Group of the UK National Screening Committee advising the Government on questions around clinical deployment of AI for screening programmes. He was awarded an ERC Starting Grant in 2017.

Academic & Industry Positions

• Since 2022: Professor, Imperial College London, UK
• Since 2021: Head of ML Research, Kheiron Medical Technologies, UK
• 2019-2022: Reader, Imperial College London, UK
• Since 2018: Adviser, HeartFlow, UK
• 2019-2021: Visiting Researcher, Microsoft Research Cambridge, UK
• 2017-2019: Senior Lecturer, Imperial College London, UK
• 2013-2017: Lecturer, Imperial College London, UK
• 2010-2013: Postdoctoral Researcher, Microsoft Research Cambridge, UK
• 2010-2012: Research Fellow, Darwin College, University of Cambridge, UK
• 2006-2010: Research Assistant, Technische Universitaet Muenchen, Germany
• 2006-2010: Visiting Researcher, Ecole Centrale Paris, France

Awards & Honours

MICCAI MLCN Workshop Best Paper Award 2022 Awarded for our work Automatic lesion analysis of traumatic brain injury

MICCAI UNSURE Workshop Best Paper Award 2021 Awarded for our work Confidence-based Out-of-Distribution Detection

MICCAI DART Workshop Best Paper Award – Runner-Up 2021 Awarded for our work Transductive Image Segmentation

Imperial President’s Award for Outstanding Research Team 2019 Awarded to the BioMedIA Group

Philips Impact Award – MIDL 2018 Awarded for our work NeuroNet

Member of the World Economic Forum’s Young Scientists Community Selected as “extraordinary scientist under the age of 40”

NVIDIA Global Impact Award – Honorable Mention 2016 Awarded for our work on brain lesion segmentation using deep learning

ERCIM Cor Baayen Award – Honorable Mention 2013 Awarded to promising young researchers in the field of Informatics and Applied Mathematics

Medical Image Analysis – MICCAI Best Paper Award 2013 Awarded for our work Neighbourhood Approximation using Randomized Forests

Werner von Siemens Excellence Award 2007 Awarded for the diploma thesis

Francois Erbsmann Prize 2007 Awarded for the best oral presentation among all first-time presenters at IPMI 2007

Computational Challenge Awards

Winner of the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2017 Kamnitsas et al. Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Winner of the Ischemic Stroke Lesion Segmentation Challenge (ISLES) 2015 Kamnitsas et al. Multi-Scale 3D Convolutional Neural Networks for Lesion Segmentation in Brain MRI

Winner of the Automatic Intervertebral Disc Segmentation Challenge 2015 Lopez-Andrade and Glocker. Complementary Classification Forests with Graph-cut Refinement for Accurate Intervertebral Disc Localization and Segmentation

Special Award a.k.a. “Left Field Award” at the Workshop Challenge on Segmentation: Algorithms, Theory and Applications (SATA) Zikic et al. Multi-Atlas Label Propagation with Atlas Encoding by Randomized Forests

Winner of the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2012 Zikic et al. Context-sensitive Classification Forests for Segmentation of Brain Tumor Tissues