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Biomechanical modelling of brain atrophy through deep learning
We present a proof-of-concept, deep learning (DL) based, differentiable ...
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Test-time Unsupervised Domain Adaptation
Convolutional neural networks trained on publicly available medical imag...
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Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE
The increasing efficiency and compactness of deep learning architectures...
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A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality
Quality control (QC) of medical images is essential to ensure that downs...
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On the Initialization of Long Short-Term Memory Networks
Weight initialization is important for faster convergence and stability ...
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Towards Quantifying Neurovascular Resilience
Whilst grading neurovascular abnormalities is critical for prompt surgic...
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Privacy-preserving Federated Brain Tumour Segmentation
Due to medical data privacy regulations, it is often infeasible to colle...
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Let's agree to disagree: learning highly debatable multirater labelling
Classification and differentiation of small pathological objects may gre...
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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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Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning
The ability to synthesise Computed Tomography images - commonly known as...
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Robust parametric modeling of Alzheimer's disease progression
Quantitative characterization of disease progression using longitudinal ...
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As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging
Counting is a fundamental task in biomedical imaging and count is an imp...
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Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Quantification of cerebral white matter hyperintensities (WMH) of presum...
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Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
Disease progression modeling (DPM) using longitudinal data is a challeng...
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A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Semantic segmentation of medical images aims to associate a pixel with a...
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3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects
Extremely small objects (ESO) have become observable on clinical routine...
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Elastic Registration of Geodesic Vascular Graphs
Vascular graphs can embed a number of high-level features, from morpholo...
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Deep Boosted Regression for MR to CT Synthesis
Attenuation correction is an essential requirement of positron emission ...
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Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
Segmenting vascular pathologies such as white matter lesions in Brain ma...
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Robust training of recurrent neural networks to handle missing data for disease progression modeling
Disease progression modeling (DPM) using longitudinal data is a challeng...
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PIMMS: Permutation Invariant Multi-Modal Segmentation
In a research context, image acquisition will often involve a pre-define...
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Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions
Automated medical image segmentation, specifically using deep learning, ...
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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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VTrails: Inferring Vessels with Geodesic Connectivity Trees
The analysis of vessel morphology and connectivity has an impact on a nu...
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NiftyNet: a deep-learning platform for medical imaging
Medical image analysis and computer-assisted intervention problems are i...
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An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation
Deep convolutional neural networks (CNNs) have shown excellent performan...
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Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
Deep-learning has proved in recent years to be a powerful tool for image...
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On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
Deep convolutional neural networks are powerful tools for learning visua...
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Generative diffeomorphic atlas construction from brain and spinal cord MRI data
In this paper we will focus on the potential and on the challenges assoc...
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