
-
TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
We present TorchIO, an open-source Python library for efficient loading,...
read it
-
A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology
We introduce a method for training neural networks to perform image or v...
read it
-
Model-Based and Data-Driven Strategies in Medical Image Computing
Model-based approaches for image reconstruction, analysis and interpreta...
read it
-
dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
AUTOMAP is a promising generalized reconstruction approach, however, it ...
read it
-
Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures
The use of machine learning (ML) algorithms has significantly increased ...
read it
-
Active Crowd Counting with Limited Supervision
To learn a reliable people counter from crowd images, head center annota...
read it
-
Topology-preserving augmentation for CNN-based segmentation of congenital heart defects from 3D paediatric CMR
Patient-specific 3D printing of congenital heart anatomy demands an accu...
read it
-
Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction
We present simple reconstruction networks for multi-coil data by extendi...
read it
-
V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation
Atrial Fibrillation (AF) is a common electro-physiological cardiac disor...
read it
-
Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
Despite the state-of-the-art performance for medical image segmentation,...
read it
-
Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels
The performance of multi-task learning in Convolutional Neural Networks ...
read it
-
Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE
The increasing efficiency and compactness of deep learning architectures...
read it
-
Spectral Multi-scale Community Detection in Temporal Networks with an Application
The analysis of temporal networks has a wide area of applications in a w...
read it
-
Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with Bayesian inference for uncertainty-based quality-control
Tissue characterisation with CMR parametric mapping has the potential to...
read it
-
Explaining Deep Neural Networks Using Spectrum-Based Fault Localization
Deep neural networks (DNNs) increasingly replace traditionally developed...
read it
-
Channel Assignment in Uplink Wireless Communication using Machine Learning Approach
This letter investigates a channel assignment problem in uplink wireless...
read it
-
Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging
Deep neural networks exhibit limited generalizability across images with...
read it
-
A Survey of Conventional and Artificial Intelligence / Learning based Resource Allocation and Interference Mitigation Schemes in D2D Enabled Networks
5th generation networks are envisioned to provide seamless and ubiquitou...
read it
-
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation
Automatic brain tumor segmentation plays an important role for diagnosis...
read it
-
3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects
Extremely small objects (ESO) have become observable on clinical routine...
read it
-
Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations
The goal of this work is to train a neural network which approximates so...
read it
-
Gated Recurrent Units Learning for Optimal Deployment of Visible Light Communications Enabled UAVs
In this paper, the problem of optimizing the deployment of unmanned aeri...
read it
-
Oversegmenting Graphs
We propose a novel method to adapt a graph to image data. The method dri...
read it
-
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding
We propose a model for tagging unstructured texts with an arbitrary numb...
read it
-
Global and Local Interpretability for Cardiac MRI Classification
Deep learning methods for classifying medical images have demonstrated i...
read it
-
Test-time augmentation with uncertainty estimation for deep learning-based medical image segmentation
Data augmentation has been widely used for training deep learning system...
read it
-
Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning
Good quality of medical images is a prerequisite for the success of subs...
read it
-
Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets
Brain tissue segmentation from multimodal MRI is a key building block of...
read it
-
Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation
We propose a new deep learning method for tumour segmentation when deali...
read it
-
Robotic Tactile Perception of Object Properties: A Review
Touch sensing can help robots understand their sur- rounding environment...
read it
-
A Brief Introduction to Machine Learning for Engineers
This monograph aims at providing an introduction to key concepts, algori...
read it
-
CASP Solutions for Planning in Hybrid Domains
CASP is an extension of ASP that allows for numerical constraints to be ...
read it
-
Extracting Lifted Mutual Exclusion Invariants from Temporal Planning Domains
We present a technique for automatically extracting mutual exclusion inv...
read it
-
Two forms of minimality in ASPIC+
Many systems of structured argumentation explicitly require that the fac...
read it
-
On the links between argumentation-based reasoning and nonmonotonic reasoning
In this paper we investigate the links between instantiated argumentatio...
read it
-
PDDL+ Planning via Constraint Answer Set Programming
PDDL+ is an extension of PDDL that enables modelling planning domains wi...
read it
-
Phase Diagram of Restricted Boltzmann Machines and Generalised Hopfield Networks with Arbitrary Priors
Restricted Boltzmann Machines are described by the Gibbs measure of a bi...
read it
-
Random Forest regression for manifold-valued responses
An increasing array of biomedical and computer vision applications requi...
read it
-
Learning what to look in chest X-rays with a recurrent visual attention model
X-rays are commonly performed imaging tests that use small amounts of ra...
read it
-
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
Inspired by recent advances in deep learning, we propose a framework for...
read it
-
Gaussian Processes for Survival Analysis
We introduce a semi-parametric Bayesian model for survival analysis. The...
read it
-
Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks
Motivated by the need to automate medical information extraction from fr...
read it
-
Deep metric learning for multi-labelled radiographs
Many radiological studies can reveal the presence of several co-existing...
read it
-
Theory of Semi-Instantiation in Abstract Argumentation
We study instantiated abstract argumentation frames of the form (S,R,I),...
read it
-
Probabilistic Argumentation. An Equational Approach
There is a generic way to add any new feature to a system. It involves 1...
read it
-
The Computational Complexity of Structure-Based Causality
Halpern and Pearl introduced a definition of actual causality; Eiter and...
read it
-
Equilibrium States in Numerical Argumentation Networks
Given an argumentation network with initial values to the arguments, we ...
read it
-
Abduction and Dialogical Proof in Argumentation and Logic Programming
We develop a model of abduction in abstract argumentation, where changes...
read it
-
Learning population and subject-specific brain connectivity networks via Mixed Neighborhood Selection
In neuroimaging data analysis, Gaussian graphical models are often used ...
read it
-
Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks
Pattern recognition methods using neuroimaging data for the diagnosis of...
read it