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
Book ChapterDOI
Effiziente nichtlineare Registrierung mittels diskreter Optimierung
Ben Glocker,Ben Glocker,Nikos Komodakis,Nikos Komodakis,Nikos Paragios,Georgios Tziritas,Nassir Navab +6 more
TL;DR: Das klassische Energieminimierungsproblem Intensitats-basierter Methoden in eine Markov Random Field Formulierung eingebettet werden als Transformationsmodell in Betracht gezogen and die Registrierung wird so auf ein diskretes Labeling-Problem reduziert.
Deep Learning Methods for Estimating "Brain Age" from Structural MRI Scans
TL;DR: A framework based on Deep Gaussian Processes is devised which achieves state-of-the-art results in terms of global brain age prediction and is introduced the first ever attempt of predicting brain age at voxel-level using context-sensitive Random Forests.
Book ChapterDOI
The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification
Vasileios Baltatzis,Kyriaki-Margarita Bintsi,Loic Le Folgoc,Octavio E. Martinez Manzanera,Sam Ellis,Arjun Nair,Sujal R. Desai,Ben Glocker,Julia A. Schnabel +8 more
TL;DR: In this paper, the effect of truthing when aggregating labels from multiple experts is investigated. But the authors find that specific choices can have severe impact on the data distribution where it may be possible to achieve superior performance on one sample distribution but not on another.
Posted Content
Cranial Implant Design via Virtual Craniectomy with Shape Priors
TL;DR: This work proposes and evaluates alternative automatic deep learning models for cranial implant reconstruction from CT images using the database released by the AutoImplant challenge, and compared to a baseline implemented by the organizers.
Posted Content
Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes
Vanya V. Valindria,Ioannis Lavdas,Juan J. Cerrolaza,Eric O. Aboagye,Andrea Rockall,Daniel Rueckert,Ben Glocker +6 more
TL;DR: In this article, a two-stage approach with weighting schemes based on auto-context and spatial atlas priors is proposed to boost the segmentation accuracy of multiple small organs in whole-body MRI scans.