b'Ben Glocker'

research

∙ 01/19/2022

Investigating underdiagnosis of AI algorithms in the presence of multiple sources of dataset bias

Deep learning models have shown great potential for image-based diagnosi...

Melanie Bernhardt, et al. ∙

research

∙ 01/08/2022

CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwnannoma and Cochlea Segmentation

Domain Adaptation (DA) has recently raised strong interests in the medic...

Reuben Dorent, et al. ∙

research

∙ 10/27/2021

Algorithmic encoding of protected characteristics and its implications on disparities across subgroups

It has been rightfully emphasized that the use of AI for clinical decisi...

Ben Glocker, et al. ∙

research

∙ 10/08/2021

Is MC Dropout Bayesian?

MC Dropout is a mainstream "free lunch" method in medical imaging for ap...

Loic Le Folgoc, et al. ∙

research

∙ 09/30/2021

DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization

In recent years, the research landscape of machine learning in medical i...

Turkay Kart, et al. ∙

research

∙ 09/11/2021

Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition

Despite impressive accuracy, deep neural networks are often miscalibrate...

Mobarakol Islam, et al. ∙

research

∙ 08/11/2021

The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification

Using publicly available data to determine the performance of methodolog...

Vasileios Baltatzis, et al. ∙

research

∙ 08/10/2021

The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data

Convolutional Neural Networks (CNNs) are widely used for image classific...

Vasileios Baltatzis, et al. ∙

research

∙ 07/31/2021

Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data

Datasets are rarely a realistic approximation of the target population. ...

Loic Le Folgoc, et al. ∙

research

∙ 07/20/2021

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions

Despite technological and medical advances, the detection, interpretatio...

Richard Osuala, et al. ∙

research

∙ 07/19/2021

Transductive image segmentation: Self-training and effect of uncertainty estimation

Semi-supervised learning (SSL) uses unlabeled data during training to le...

Konstantinos Kamnitsas, et al. ∙

research

∙ 07/16/2021

Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification

Whole body magnetic resonance imaging (WB-MRI) is the recommended modali...

Talha Qaiser, et al. ∙

research

∙ 07/13/2021

Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift

Scarcity of high quality annotated images remains a limiting factor for ...

Gregory Filbrandt, et al. ∙

research

∙ 07/06/2021

Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis

Image classification models deployed in the real world may receive input...

Christoph Berger, et al. ∙

research

∙ 04/28/2021

Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation

We propose a parameter efficient Bayesian layer for hierarchical convolu...

Sebastian G. Popescu, et al. ∙

research

∙ 04/12/2021

Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotations

The task of image segmentation is inherently noisy due to ambiguities re...

Mobarakol Islam, et al. ∙

research

∙ 04/12/2021

Common Limitations of Image Processing Metrics: A Picture Story

While the importance of automatic image analysis is increasing at an eno...

Annika Reinke, et al. ∙

research

∙ 02/20/2021

Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation

Class imbalance poses a challenge for developing unbiased, accurate pred...

Zeju Li, et al. ∙

research

∙ 12/18/2020

Atlas-ISTN: Joint Segmentation, Registration and Atlas Construction with Image-and-Spatial Transformer Networks

Deep learning models for semantic segmentation are able to learn powerfu...

Matthew Sinclair, et al. ∙

research

∙ 10/28/2020

Hierarchical Gaussian Processes with Wasserstein-2 Kernels

We investigate the usefulness of Wasserstein-2 kernels in the context of...

Sebastian Popescu, et al. ∙

research

∙ 09/29/2020

Cranial Implant Design via Virtual Craniectomy with Shape Priors

Cranial implant design is a challenging task, whose accuracy is crucial ...

Franco Matzkin, et al. ∙

research

∙ 07/07/2020

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

Decompressive craniectomy (DC) is a common surgical procedure consisting...

Franco Matzkin, et al. ∙

research

∙ 06/24/2020

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

We introduce Post-DAE, a post-processing method based on denoising autoe...

Agostina J. Larrazabal, et al. ∙

research

∙ 06/11/2020

Deep Structural Causal Models for Tractable Counterfactual Inference

We formulate a general framework for building structural causal models (...

Nick Pawlowski, et al. ∙

research

∙ 06/10/2020

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty

In image segmentation, there is often more than one plausible solution f...

Miguel Monteiro, et al. ∙

research

∙ 01/06/2020

Unpaired Multi-modal Segmentation via Knowledge Distillation

Multi-modal learning is typically performed with network architectures c...

Qi Dou, et al. ∙

research

∙ 12/17/2019

Causality matters in medical imaging

This article discusses how the language of causality can shed new light ...

Daniel Coelho de Castro, et al. ∙

research

∙ 11/23/2019

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

Convolutional Neural Networks (CNNs) used on image classification tasks ...

CK, et al. ∙

research

∙ 10/29/2019

Domain Generalization via Model-Agnostic Learning of Semantic Features

Generalization capability to unseen domains is crucial for machine learn...

Qi Dou, et al. ∙

research

∙ 10/14/2019

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...

James McCouat, et al. ∙

research

∙ 10/10/2019

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 ...

Ben Glocker, et al. ∙

research

∙ 08/16/2019

Needles in Haystacks: On Classifying Tiny Objects in Large Images

In some computer vision domains, such as medical or hyperspectral imagin...

Nick Pawlowski, et al. ∙

research

∙ 07/25/2019

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...

Zeju Li, et al. ∙

research

∙ 07/25/2019

Is Texture Predictive for Age and Sex in Brain MRI?

Deep learning builds the foundation for many medical image analysis task...

Nick Pawlowski, et al. ∙

research

∙ 07/22/2019

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

Image registration with deep neural networks has become an active field ...

Matthew C. H. Lee, et al. ∙

research

∙ 06/05/2019

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

Accurate, automated lesion detection in Computed Tomography (CT) is an i...

Martin Zlocha, et al. ∙

research

∙ 05/18/2019

Quantitative Error Prediction of Medical Image Registration using Regression Forests

Predicting registration error can be useful for evaluation of registrati...

Hessam Sokooti, et al. ∙

research

∙ 05/14/2019

Graph Convolutional Gaussian Processes

We propose a novel Bayesian nonparametric method to learn translation-in...

Ian Walker, et al. ∙

research

∙ 03/06/2019

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

We investigate discrete spin transformations, a geometric framework to m...

Loic Le Folgoc, et al. ∙

research

∙ 03/05/2019

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...

Daniel Grzech, et al. ∙

research

∙ 01/27/2019

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...

Robert Robinson, et al. ∙

research

∙ 12/19/2018

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...

Qi Dou, et al. ∙

research

∙ 11/06/2018

Towards continual learning in medical imaging

This work investigates continual learning of two segmentation tasks in b...

Chaitanya Baweja, et al. ∙

research

∙ 09/27/2018

Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

Revealing latent structure in data is an active field of research, havin...

Daniel Coelho de Castro, et al. ∙

research

∙ 08/22/2018

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

We propose a novel attention gate (AG) model for medical image analysis ...

Jo Schlemper, et al. ∙

research

∙ 07/30/2018

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...

Vanya V. Valindria, et al. ∙

research

∙ 07/09/2018

High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks

The ability to generate synthetic medical images is useful for data augm...

Dimitrios Korkinof, et al. ∙

research

∙ 06/16/2018

Real-time Prediction of Segmentation Quality

Recent advances in deep learning based image segmentation methods have e...

Robert Robinson, et al. ∙

research

∙ 06/14/2018

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

Recent advances in deep learning led to novel generative modeling techni...

Xiaoran Chen, et al. ∙

research

∙ 06/11/2018

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

NeuroNet is a deep convolutional neural network mimicking multiple popul...

Martin Rajchl, et al. ∙

Ben Glocker

Featured Co-authors