Ben Glocker

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Reader in Machine Learning for Imaging at Imperial College London.

My research is at the intersection of medical image analysis and artificial intelligence aiming to build computational tools for improving image-based detection and diagnosis of disease.

Hierarchical Gaussian Processes with Wasserstein-2 Kernels

We investigate the usefulness of Wasserstein-2 kernels in the context of...
10/28/2020 ∙ by Sebastian Popescu, et al. ∙ 0 ∙ share

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Cranial Implant Design via Virtual Craniectomy with Shape Priors

Cranial implant design is a challenging task, whose accuracy is crucial ...
09/29/2020 ∙ by Franco Matzkin, et al. ∙ 8 ∙ share

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Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy

Decompressive craniectomy (DC) is a common surgical procedure consisting...
07/07/2020 ∙ by Franco Matzkin, et al. ∙ 13 ∙ share

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Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders

We introduce Post-DAE, a post-processing method based on denoising autoe...
06/24/2020 ∙ by Agostina J. Larrazabal, et al. ∙ 1 ∙ share

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Deep Structural Causal Models for Tractable Counterfactual Inference

We formulate a general framework for building structural causal models (...
06/11/2020 ∙ by Nick Pawlowski, et al. ∙ 48 ∙ share

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Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty

In image segmentation, there is often more than one plausible solution f...
06/10/2020 ∙ by Miguel Monteiro, et al. ∙ 32 ∙ share

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Unpaired Multi-modal Segmentation via Knowledge Distillation

Multi-modal learning is typically performed with network architectures c...
01/06/2020 ∙ by Qi Dou, et al. ∙ 23 ∙ share

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Causality matters in medical imaging

This article discusses how the language of causality can shed new light ...
12/17/2019 ∙ by Daniel Coelho de Castro, et al. ∙ 24 ∙ share

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Universal Adversarial Perturbations to Understand Robustness of Texture vs. Shape-biased Training

Convolutional Neural Networks (CNNs) used on image classification tasks ...
11/23/2019 ∙ by Kenneth T. Co, et al. ∙ 18 ∙ share

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Domain Generalization via Model-Agnostic Learning of Semantic Features

Generalization capability to unseen domains is crucial for machine learn...
10/29/2019 ∙ by Qi Dou, et al. ∙ 21 ∙ share

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Vertebrae Detection and Localization in CT with Two-Stage CNNs and Dense Annotations

We propose a new, two-stage approach to the vertebrae centroid detection...
10/14/2019 ∙ by James McCouat, et al. ∙ 19 ∙ share

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Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects

This is an empirical study to investigate the impact of scanner effects ...
10/10/2019 ∙ by Ben Glocker, et al. ∙ 22 ∙ share

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Needles in Haystacks: On Classifying Tiny Objects in Large Images

In some computer vision domains, such as medical or hyperspectral imagin...
08/16/2019 ∙ by Nick Pawlowski, et al. ∙ 6 ∙ share

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

Overfitting in deep learning has been the focus of a number of recent wo...
07/25/2019 ∙ by Zeju Li, et al. ∙ 6 ∙ share

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Is Texture Predictive for Age and Sex in Brain MRI?

Deep learning builds the foundation for many medical image analysis task...
07/25/2019 ∙ by Nick Pawlowski, et al. ∙ 4 ∙ share

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

Image registration with deep neural networks has become an active field ...
07/22/2019 ∙ by Matthew C. H. Lee, et al. ∙ 2 ∙ share

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

Accurate, automated lesion detection in Computed Tomography (CT) is an i...
06/05/2019 ∙ by Martin Zlocha, et al. ∙ 5 ∙ share

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Quantitative Error Prediction of Medical Image Registration using Regression Forests

Predicting registration error can be useful for evaluation of registrati...
05/18/2019 ∙ by Hessam Sokooti, et al. ∙ 0 ∙ share

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Graph Convolutional Gaussian Processes

We propose a novel Bayesian nonparametric method to learn translation-in...
05/14/2019 ∙ by Ian Walker, et al. ∙ 2 ∙ share

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Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing

We investigate discrete spin transformations, a geometric framework to m...
03/06/2019 ∙ by Loic Le Folgoc, et al. ∙ 0 ∙ share

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FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms

We present a new approach to diffeomorphic non-rigid registration of med...
03/05/2019 ∙ by Daniel Grzech, et al. ∙ 8 ∙ share

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Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study

Background: The trend towards large-scale studies including population i...
01/27/2019 ∙ by Robert Robinson, et al. ∙ 38 ∙ share

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PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation

Deep convolutional networks have demonstrated the state-of-the-art perfo...
12/19/2018 ∙ by Qi Dou, et al. ∙ 8 ∙ share

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Towards continual learning in medical imaging

This work investigates continual learning of two segmentation tasks in b...
11/06/2018 ∙ by Chaitanya Baweja, et al. ∙ 16 ∙ share

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Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

Revealing latent structure in data is an active field of research, havin...
09/27/2018 ∙ by Daniel Coelho de Castro, et al. ∙ 22 ∙ share

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Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images

We propose a novel attention gate (AG) model for medical image analysis ...
08/22/2018 ∙ by Jo Schlemper, et al. ∙ 6 ∙ share

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Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes

Accurate and robust segmentation of small organs in whole-body MRI is di...
07/30/2018 ∙ by Vanya V. Valindria, et al. ∙ 10 ∙ share

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High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks

The ability to generate synthetic medical images is useful for data augm...
07/09/2018 ∙ by Dimitrios Korkinof, et al. ∙ 2 ∙ share

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Real-time Prediction of Segmentation Quality

Recent advances in deep learning based image segmentation methods have e...
06/16/2018 ∙ by Robert Robinson, et al. ∙ 14 ∙ share

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Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging

Recent advances in deep learning led to novel generative modeling techni...
06/14/2018 ∙ by Xiaoran Chen, et al. ∙ 4 ∙ share

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NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines

NeuroNet is a deep convolutional neural network mimicking multiple popul...
06/11/2018 ∙ by Martin Rajchl, et al. ∙ 8 ∙ share

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Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents

We propose a fully automatic method to find standardized view planes in ...
06/08/2018 ∙ by Amir Alansary, et al. ∙ 10 ∙ share

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Semi-Supervised Learning via Compact Latent Space Clustering

We present a novel cost function for semi-supervised learning of neural ...
06/07/2018 ∙ by Konstantinos Kamnitsas, et al. ∙ 2 ∙ share

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Nonparametric Density Flows for MRI Intensity Normalisation

With the adoption of powerful machine learning methods in medical image ...
06/07/2018 ∙ by Daniel Coelho de Castro, et al. ∙ 0 ∙ share

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Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity

Graph convolutional networks (GCNs) allow to apply traditional convoluti...
06/05/2018 ∙ by Salim Arslan, et al. ∙ 10 ∙ share

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Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease

Graphs are widely used as a natural framework that captures interactions...
06/05/2018 ∙ by Sarah Parisot, et al. ∙ 0 ∙ share

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Domain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy

The variations in multi-center data in medical imaging studies have brou...
06/01/2018 ∙ by Vanya V. Valindria, et al. ∙ 0 ∙ share

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Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry

Pose estimation, i.e. predicting a 3D rigid transformation with respect ...
05/02/2018 ∙ by Benjamin Hou, et al. ∙ 22 ∙ share

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Attention-Gated Networks for Improving Ultrasound Scan Plane Detection

In this work, we apply an attention-gated network to real-time automated...
04/15/2018 ∙ by Jo Schlemper, et al. ∙ 0 ∙ share

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

We propose a novel attention gate (AG) model for medical imaging that au...
04/11/2018 ∙ by Ozan Oktay, et al. ∙ 0 ∙ share

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Learning-Based Quality Control for Cardiac MR Images

The effectiveness of a cardiovascular magnetic resonance (CMR) scan depe...
03/25/2018 ∙ by Giacomo Tarroni, et al. ∙ 0 ∙ share

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DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

We present DLTK, a toolkit providing baseline implementations for effici...
11/18/2017 ∙ by Nick Pawlowski, et al. ∙ 0 ∙ share

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Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Deep learning approaches such as convolutional neural nets have consiste...
11/04/2017 ∙ by Konstantinos Kamnitsas, et al. ∙ 0 ∙ share

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Implicit Weight Uncertainty in Neural Networks

We interpret HyperNetworks within the framework of variational inference...
11/03/2017 ∙ by Nick Pawlowski, et al. ∙ 0 ∙ share

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Human-level CMR image analysis with deep fully convolutional networks

Cardiovascular magnetic resonance (CMR) imaging is a standard imaging mo...
10/25/2017 ∙ by Wenjia Bai, et al. ∙ 0 ∙ share

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3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images

Limited capture range and the requirement to provide high quality initia...
09/19/2017 ∙ by Benjamin Hou, et al. ∙ 0 ∙ share

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

Incorporation of prior knowledge about organ shape and location is key t...
05/22/2017 ∙ by Ozan Oktay, et al. ∙ 0 ∙ share

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Efficient variational Bayesian neural network ensembles for outlier detection

In this work we perform outlier detection using ensembles of neural netw...
03/20/2017 ∙ by Nick Pawlowski, et al. ∙ 0 ∙ share

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Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease...
03/08/2017 ∙ by Sarah Parisot, et al. ∙ 0 ∙ share

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Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Evaluating similarity between graphs is of major importance in several c...
03/07/2017 ∙ by Ira Ktena, et al. ∙ 0 ∙ share

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Ben Glocker

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