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09/07/2023
CenTime: Event-Conditional Modelling of Censoring in Survival Analysis
Survival analysis is a valuable tool for estimating the time until speci...
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08/10/2023
A coupled-mechanisms modelling framework for neurodegeneration
Computational models of neurodegeneration aim to emulate the evolving pa...
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07/29/2023
A 3D deep learning classifier and its explainability when assessing coronary artery disease
Early detection and diagnosis of coronary artery disease (CAD) could sav...
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07/29/2023
A data-centric deep learning approach to airway segmentation
The morphology and distribution of airway tree abnormalities enables dia...
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07/13/2023
Rician likelihood loss for quantitative MRI using self-supervised deep learning
Purpose: Previous quantitative MR imaging studies using self-supervised ...
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05/05/2023
Domain-agnostic segmentation of thalamic nuclei from joint structural and diffusion MRI
The human thalamus is a highly connected subcortical grey-matter structu...
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05/02/2023
Expectation Maximization Pseudo Labelling for Segmentation with Limited Annotations
We study pseudo labelling and its generalisation for semi-supervised seg...
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04/26/2023
Low-field magnetic resonance image enhancement via stochastic image quality transfer
Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in wide...
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03/19/2023
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study
In this study, we present a hybrid CNN-RNN approach to investigate long-...
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02/25/2023
DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification
Recent advances in MRI have led to the creation of large datasets. With ...
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10/17/2022
Deformably-Scaled Transposed Convolution
Transposed convolution is crucial for generating high-resolution outputs...
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10/13/2022
An Experiment Design Paradigm using Joint Feature Selection and Task Optimization
This paper presents a subsampling-task paradigm for data-driven task-spe...
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10/05/2022
Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine Learning
Machine learning is a powerful approach for fitting microstructural mode...
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08/22/2022
Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors
During the COVID-19 pandemic, the sheer volume of imaging performed in a...
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08/08/2022
Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation
This paper concerns pseudo labelling in segmentation. Our contribution i...
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06/07/2022
An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training
Large medical imaging data sets are becoming increasingly available. A c...
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03/30/2022
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning
Lung cancer is the leading cause of cancer-related mortality worldwide. ...
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03/21/2022
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data
Idiopathic Pulmonary Fibrosis (IPF) is an inexorably progressive fibroti...
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03/19/2022
Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation
We propose MisMatch, a novel consistency-driven semi-supervised segmenta...
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03/17/2022
Progressive Subsampling for Oversampled Data – Application to Quantitative MRI
We present PROSUB: PROgressive SUBsampling, a deep learning based, autom...
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03/12/2022
VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation
Estimating clinically-relevant vascular features following vessel segmen...
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12/15/2021
Ten years of image analysis and machine learning competitions in dementia
Machine learning methods exploiting multi-parametric biomarkers, especia...
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11/19/2021
Evaluation of automated airway morphological quantification for assessing fibrosing lung disease
Abnormal airway dilatation, termed traction bronchiectasis, is a typical...
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10/23/2021
MisMatch: Learning to Change Predictive Confidences with Attention for Consistency-Based, Semi-Supervised Medical Image Segmentation
The lack of labels is one of the fundamental constraints in deep learnin...
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09/22/2021
Learning to Downsample for Segmentation of Ultra-High Resolution Images
Segmentation of ultra-high resolution images with deep learning is chall...
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04/02/2021
Uncertainty-Aware Annotation Protocol to Evaluate Deformable Registration Algorithms
Landmark correspondences are a widely used type of gold standard in imag...
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12/24/2020
Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast
Most existing algorithms for automatic 3D morphometry of human brain MRI...
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11/04/2020
DeepReg: a deep learning toolkit for medical image registration
DeepReg (https://github.com/DeepRegNet/DeepReg) is a community-supported...
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11/02/2020
Learning transition times in event sequences: the Event-Based Hidden Markov Model of disease progression
Progressive diseases worsen over time and are characterised by monotonic...
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07/31/2020
Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
Recent years have seen increasing use of supervised learning methods for...
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07/29/2020
Learning To Pay Attention To Mistakes
As evidenced in visual results in <cit.><cit.><cit.><cit.><cit.>, the pe...
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07/29/2020
Foveation for Segmentation of Ultra-High Resolution Images
Segmentation of ultra-high resolution images is challenging because of t...
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03/16/2020
Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients
1.5T or 3T scanners are the current standard for clinical MRI, but low-f...
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02/09/2020
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
We present the findings of "The Alzheimer's Disease Prediction Of Longit...
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01/23/2020
TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data
The TADPOLE Challenge compares the performance of algorithms at predicti...
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12/03/2019
Degenerative Adversarial NeuroImage Nets for 3D Simulations: Application in Longitudinal MRI
The recent success of deep learning together with the availability of la...
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09/15/2019
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator
MR images scanned at low magnetic field (<1T) have lower resolution in t...
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08/26/2019
Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels
The performance of multi-task learning in Convolutional Neural Networks ...
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07/31/2019
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement
Deep learning (DL) has shown great potential in medical image enhancemen...
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07/26/2019
Multi-Stage Prediction Networks for Data Harmonization
In this paper, we introduce multi-task learning (MTL) to data harmonizat...
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07/05/2019
Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression
Simulating images representative of neurodegenerative diseases is import...
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05/21/2019
BrainPainter: A software for the visualisation of brain structures, biomarkers and associated pathological processes
We present BrainPainter, a software that automatically generates images ...
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02/10/2019
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion
The predictive performance of supervised learning algorithms depends on ...
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01/11/2019
DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders
Here we present DIVE: Data-driven Inference of Vertexwise Evolution. DIV...
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01/11/2019
Disease Knowledge Transfer across Neurodegenerative Diseases
We introduce Disease Knowledge Transfer (DKT), a novel technique for tra...
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08/16/2018
Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images
In this paper we address the memory demands that come with the processin...
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07/17/2018
Adaptive Neural Trees
Deep neural networks and decision trees operate on largely separate para...
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06/19/2018
A generative model of realistic brain cells with application to numerical simulation of diffusion-weighted MR signal
In this work, we introduce a novel computational framework that we devel...
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06/18/2018
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
Multi-task neural network architectures provide a mechanism that jointly...
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05/10/2018