activity
20172022
most citedDLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

74 citations · 302 across the 26 of their papers we have counts for

collaborators
Showing 2019Show all

9 papers · 1 filter

eess.IV2019

Is Texture Predictive for Age and Sex in Brain MRI?

Nick Pawlowski, Ben Glocker

Deep learning builds the foundation for many medical image analysis tasks where neuralnetworks are often designed to have a large receptive field to incorporate long spatialdepende…

cs.CV20199 cited

Image-and-Spatial Transformer Networks for Structure-Guided Image Registration

Matthew C. H. Lee, Ozan Oktay, Andreas Schuh +2

Image registration with deep neural networks has become an active field of research and exciting avenue for a long standing problem in medical imaging. The goal is to learn a compl…

cs.LG2019

Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation

Zeju Li, Konstantinos Kamnitsas, Ben Glocker

Overfitting in deep learning has been the focus of a number of recent works, yet its exact impact on the behavior of neural networks is not well understood. This study analyzes ove…

eess.IV201922 cited

Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels

Martin Zlocha, Qi Dou, Ben Glocker

Accurate, automated lesion detection in Computed Tomography (CT) is an important yet challenging task due to the large variation of lesion types, sizes, locations and appearances.…

eess.IV201938 cited

Quantitative Error Prediction of Medical Image Registration using Regression Forests

Hessam Sokooti, Gorkem Saygili, Ben Glocker +2

Predicting registration error can be useful for evaluation of registration procedures, which is important for the adoption of registration techniques in the clinic. In addition, qu…

cs.LG20199 cited

Graph Convolutional Gaussian Processes

Ian Walker, Ben Glocker

We propose a novel Bayesian nonparametric method to learn translation-invariant relationships on non-Euclidean domains. The resulting graph convolutional Gaussian processes can be…