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Computer Science

D-Index
75
Citations
40761
World Ranking
1372
National Ranking
80

Overview

Ben Glocker is affiliated with Imperial College London in the United Kingdom. Their research spans the fields of Medicine and Computer Science, with a particular focus on subfields such as Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Neurology, and Epidemiology.

The main topics of Glocker's research work include:

  • Radiomics and Machine Learning in Medical Imaging
  • Traumatic Brain Injury and Neurovascular Disturbances
  • Artificial Intelligence in Healthcare and Education
  • AI in cancer detection
  • Traumatic Brain Injury Research
  • Advanced Neural Network Applications
  • Medical Image Segmentation Techniques

Recent papers authored or co-authored by Glocker cover various aspects of clinical prediction models and AI-driven systems in medical contexts. Notable publications include:

  • "TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods," 2024, BMJ
  • "Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI," 2022, Nature Medicine
  • "VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images," 2021, Medical Image Analysis
  • "Metrics reloaded: recommendations for image analysis validation," 2024, Nature Methods
  • "Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study," 2021, npj Digital Medicine

Glocker frequently collaborates with co-authors including Ari Ercole, Giuseppe Citerio, Endre Czeiter, Bart Depreitere, and Marta Correia, each representing multiple joint publications.

Their work appears regularly in venues such as:

  • arXiv (Cornell University)
  • Journal of Neurotrauma
  • Medical Image Analysis
  • Nature Medicine
  • Lecture notes in computer science

Best Publications

  • The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

    Bjoern H. Menze;Andras Jakab;Stefan Bauer;Jayashree Kalpathy-Cramer

  • Attention U-Net: Learning Where to Look for the Pancreas

    Ozan Oktay;Jo Schlemper;Loïc Le Folgoc;Matthew C. H. Lee

  • Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

    Konstantinos Kamnitsas;Christian Ledig;Virginia F.J. Newcombe;Joanna P. Simpson

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

  • Attention gated networks: Learning to leverage salient regions in medical images.

    Jo Schlemper;Ozan Oktay;Michiel Schaap;Mattias P. Heinrich

  • Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images

    Jamie Shotton;Ben Glocker;Christopher Zach;Shahram Izadi

  • ElasticFusion: Dense SLAM Without A Pose Graph

    Thomas Whelan;Stefan Leutenegger;Renato F. Salas-Moreno;Ben Glocker

  • Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation

    Ozan Oktay;Enzo Ferrante;Konstantinos Kamnitsas;Mattias Heinrich

  • Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

    Wenjia Bai;Matthew Sinclair;Giacomo Tarroni;Ozan Oktay

  • ElasticFusion: Real-time dense SLAM and light source estimation

    Thomas Whelan;Renato F Salas-Moreno;Ben Glocker;Andrew J Davison

  • Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease.

    Sarah Parisot;Sofia Ira Ktena;Enzo Ferrante;Matthew C. H. Lee

  • Dense image registration through MRFs and efficient linear programming.

    Ben Glocker;Nikos Komodakis;Nikos Komodakis;Georgios Tziritas;Nassir Navab

  • Causality matters in medical imaging.

    Daniel Coelho de Castro;Ian Walker;Ben Glocker

  • ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

    Oskar Maier;Bjoern H. Menze;Janina von der Gablentz;Levin Häni

  • Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge

    K. Murphy;B. van Ginneken;J. M. Reinhardt;S. Kabus

  • Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

    Konstantinos Kamnitsas;Konstantinos Kamnitsas;Christian F. Baumgartner;Christian Ledig;Virginia F. J. Newcombe

  • Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

    Konstantinos Kamnitsas;Wenjia Bai;Enzo Ferrante;Steven G. McDonagh

  • Domain Generalization via Model-Agnostic Learning of Semantic Features

    Qi Dou;Daniel Coelho de Castro;Konstantinos Kamnitsas;Ben Glocker

  • Semi-supervised learning for network-based cardiac MR image segmentation

    Wenjia Bai;Ozan Oktay;Matthew Sinclair;Hideaki Suzuki

  • Federated learning enables big data for rare cancer boundary detection

    Unknown

  • DeepMedic for Brain Tumor Segmentation

    Konstantinos Kamnitsas;Konstantinos Kamnitsas;Enzo Ferrante;Sarah Parisot;Christian Ledig

  • Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

    Ozan Oktay;Enzo Ferrante;Konstantinos Kamnitsas;Mattias Heinrich

Frequent Co-Authors

Daniel Rueckert
Daniel Rueckert Technical University of Munich
Konstantinos Kamnitsas
Konstantinos Kamnitsas University of Oxford
Antonio Criminisi
Antonio Criminisi Microsoft (United States)
Bernhard Kainz
Bernhard Kainz Imperial College London
Nikos Paragios
Nikos Paragios CentraleSupélec
Nassir Navab
Nassir Navab Technical University of Munich
Wenjia Bai
Wenjia Bai Imperial College London
Ozan Oktay
Ozan Oktay Imperial College London
Nikos Komodakis
Nikos Komodakis University of Crete

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