T
Tom Vercauteren
Researcher at King's College London
Publications - 443
Citations - 19699
Tom Vercauteren is an academic researcher from King's College London. The author has contributed to research in topics: Segmentation & Computer science. The author has an hindex of 47, co-authored 381 publications receiving 14216 citations. Previous affiliations of Tom Vercauteren include Mauna Kea Technologies & Wellcome Trust.
Papers
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Journal ArticleDOI
An unsupervised learning approach to ultrasound strain elastography with spatio-temporal consistency.
TL;DR: In this article, a recurrent network architecture with convolutional Long Short-Term Memory (convLSTM) decoder blocks is proposed to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames.
DECIDE: Diffusion-RElaxation Combined Imaging for Detailed Placental Evaluation
Andrew Melbourne,Rosalind Pratt,David R. Owen,Magdalena Sokolska,Alan Bainbridge,David Atkinson,Giles S Kendall,Jan Deprest,Tom Vercauteren,Anna L. David,Sebastien Ourselin +10 more
Posted Content
Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia.
Luis C. Garcia-Peraza-Herrera,Martin Everson,Wenqi Li,Inmanol Luengo,Lorenz Berger,Omer F. Ahmad,Laurence Lovat,Hsiu-Po Wang,Wen-Lun Wang,Rehan Haidry,Danail Stoyanov,Tom Vercauteren,Sebastien Ourselin +12 more
TL;DR: A new approach to visualise attention that aims to give some insights on those areas of the oesophageal tissue that lead a network to conclude that the images belong to a particular class and compare them with those visual features employed by clinicians to produce a clinical diagnosis.
Book ChapterDOI
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for Brain Tumor Segmentation: BraTS 2020 Challenge
TL;DR: In this paper, the authors experimented with a non-standard per-sample loss function, the generalized Wasserstein Dice loss, a nonstandard population loss function corresponding to distributionally robust optimization, and a non standard optimizer, Ranger.
Book ChapterDOI
Deep Sequential Mosaicking of Fetoscopic Videos
Sophia Bano,Francisco de Assis Guedes de Vasconcelos,Marcel Tella Amo,George Dwyer,Caspar Gruijthuijsen,Jan Deprest,Sebastien Ourselin,Emmanuel Vander Poorten,Tom Vercauteren,Danail Stoyanov +9 more
TL;DR: In this paper, a new generalized Deep Sequential Mosaicking (DSM) framework is presented for fetoscopic videos captured from different settings such as simulation, phantom, and real environments.