B
Ben Glocker
Researcher at Imperial College London
Publications - 363
Citations - 30047
Ben Glocker is an academic researcher from Imperial College London. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 60, co-authored 300 publications receiving 20402 citations. Previous affiliations of Ben Glocker include Analysis Group & Microsoft.
Papers
More filters
Posted Content
Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
Vanya V. Valindria,Ioannis Lavdas,Wenjia Bai,Konstantinos Kamnitsas,Eric O. Aboagye,Andrea Rockall,Daniel Rueckert,Ben Glocker +7 more
TL;DR: The concept of reverse classification accuracy (RCA) is introduced as a framework for predicting the performance of a segmentation method on new data and it is indicated that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth.
Posted Content
A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions.
Richard Osuala,Kaisar Kushibar,Lidia Garrucho,Akis Linardos,Zuzanna Szafranowska,Stefan Klein,Ben Glocker,Oliver Diaz,Karim Lekadir +8 more
TL;DR: In this article, the authors assess the potential of GANs to address a number of key challenges of cancer imaging, including data scarcity and imbalance, domain and dataset shifts, data access and privacy, data annotation and quantification, as well as cancer detection, tumour profiling and treatment planning.
Journal Article
Evaluation of Deep Learning to Augment Image Guided Radiotherapy for Head and Neck and Prostate Cancers
Ozan Oktay,Jay Nanavati,Anton Schwaighofer,David Carter,Melissa Bristow,Ryutaro Tanno,Gill Barnett,David W Noble,Yvonne Rimmer,Rajesh Jena,Ben Glocker,Kenton O'Hara,Christopher M. Bishop,Javier Alvarez-Valle,Aditya V. Nori +14 more
TL;DR: The study findings highlight the opportunity for widespread adoption of autosegmentation models in radiotherapy workflows to reduce overall contouring and planning time.
Posted Content
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry
Benjamin Hou,Nina Miolane,Bishesh Khanal,Bishesh Khanal,Matthew C. H. Lee,Amir Alansary,Steven McDonagh,Joseph V. Hajnal,Daniel Rueckert,Ben Glocker,Bernhard Kainz +10 more
TL;DR: In this paper, a general Riemannian formulation of the pose estimation problem is proposed to train the CNN directly on SE(3) equipped with a left-invariant metric, coupling the prediction of the translation and rotation defining the pose.
Proceedings ArticleDOI
Vector Quantisation for Robust Segmentation
TL;DR: It is proposed and justify that learning a discrete representation in a low dimensional embedding space improves robustness of a segmentation model with a dictionary learning method called vector quantisation, and improved segmentation accuracy and better robustness are demonstrated.