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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,...
03/09/2020 ∙ by Fernando Pérez-García, et al. ∙ 121 ∙ share

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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...
10/04/2019 ∙ by James R. Clough, et al. ∙ 86 ∙ share

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Model-Based and Data-Driven Strategies in Medical Image Computing

Model-based approaches for image reconstruction, analysis and interpreta...
09/23/2019 ∙ by Daniel Rueckert, et al. ∙ 30 ∙ share

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dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

AUTOMAP is a promising generalized reconstruction approach, however, it ...
09/24/2019 ∙ by Jo Schlemper, et al. ∙ 30 ∙ share

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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 ...
10/08/2019 ∙ by Jessica Dafflon, et al. ∙ 29 ∙ share

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Active Crowd Counting with Limited Supervision

To learn a reliable people counter from crowd images, head center annota...
07/13/2020 ∙ by Zhen Zhao, et al. ∙ 27 ∙ share

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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...
08/23/2019 ∙ by Nick Byrne, et al. ∙ 23 ∙ share

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Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction

We present simple reconstruction networks for multi-coil data by extendi...
09/25/2019 ∙ by Jo Schlemper, et al. ∙ 15 ∙ share

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V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation

Atrial Fibrillation (AF) is a common electro-physiological cardiac disor...
08/06/2018 ∙ by Nicolo' Savioli, et al. ∙ 14 ∙ share

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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,...
07/19/2018 ∙ by Guotai Wang, et al. ∙ 12 ∙ share

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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 ...
08/26/2019 ∙ by Felix J. S. Bragman, et al. ∙ 12 ∙ share

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Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE

The increasing efficiency and compactness of deep learning architectures...
02/13/2020 ∙ by Petru-Daniel Tudosiu, et al. ∙ 11 ∙ share

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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...
01/29/2019 ∙ by Zhana Kuncheva, et al. ∙ 10 ∙ share

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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...
01/31/2020 ∙ by Esther Puyol-Antón, et al. ∙ 9 ∙ share

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Explaining Deep Neural Networks Using Spectrum-Based Fault Localization

Deep neural networks (DNNs) increasingly replace traditionally developed...
08/06/2019 ∙ by Youcheng Sun, et al. ∙ 8 ∙ share

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Channel Assignment in Uplink Wireless Communication using Machine Learning Approach

This letter investigates a channel assignment problem in uplink wireless...
01/12/2020 ∙ by Guangyu Jia, et al. ∙ 8 ∙ share

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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...
10/30/2020 ∙ by Qingjie Meng, et al. ∙ 8 ∙ share

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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...
09/24/2018 ∙ by Kamran Zia, et al. ∙ 6 ∙ share

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Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

Automatic brain tumor segmentation plays an important role for diagnosis...
10/18/2018 ∙ by Guotai Wang, et al. ∙ 6 ∙ share

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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...
12/21/2018 ∙ by Carole H Sudre, et al. ∙ 6 ∙ share

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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...
05/02/2020 ∙ by Christopher J Arthurs, et al. ∙ 6 ∙ share

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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...
09/17/2019 ∙ by Yining Wang, et al. ∙ 5 ∙ share

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Oversegmenting Graphs

We propose a novel method to adapt a graph to image data. The method dri...
06/01/2018 ∙ by Alberto Gomez, et al. ∙ 4 ∙ share

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Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding

We propose a model for tagging unstructured texts with an arbitrary numb...
10/02/2018 ∙ by Gaurav Singh, et al. ∙ 4 ∙ share

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Global and Local Interpretability for Cardiac MRI Classification

Deep learning methods for classifying medical images have demonstrated i...
06/14/2019 ∙ by James R. Clough, et al. ∙ 4 ∙ share

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Test-time augmentation with uncertainty estimation for deep learning-based medical image segmentation

Data augmentation has been widely used for training deep learning system...
07/19/2018 ∙ by Guotai Wang, et al. ∙ 2 ∙ share

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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...
10/29/2018 ∙ by lkay Oksuz, et al. ∙ 2 ∙ share

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Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets

Brain tissue segmentation from multimodal MRI is a key building block of...
07/07/2019 ∙ by Reuben Dorent, et al. ∙ 2 ∙ share

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Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation

We propose a new deep learning method for tumour segmentation when deali...
07/25/2019 ∙ by Reuben Dorent, et al. ∙ 2 ∙ share

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Robotic Tactile Perception of Object Properties: A Review

Touch sensing can help robots understand their sur- rounding environment...
11/10/2017 ∙ by Shan Luo, et al. ∙ 0 ∙ share

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A Brief Introduction to Machine Learning for Engineers

This monograph aims at providing an introduction to key concepts, algori...
09/08/2017 ∙ by Osvaldo Simeone, et al. ∙ 0 ∙ share

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CASP Solutions for Planning in Hybrid Domains

CASP is an extension of ASP that allows for numerical constraints to be ...
04/12/2017 ∙ by Marcello Balduccini, et al. ∙ 0 ∙ share

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Extracting Lifted Mutual Exclusion Invariants from Temporal Planning Domains

We present a technique for automatically extracting mutual exclusion inv...
02/07/2017 ∙ by Sara Bernardini, et al. ∙ 0 ∙ share

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Two forms of minimality in ASPIC+

Many systems of structured argumentation explicitly require that the fac...
02/02/2017 ∙ by Zimi Li, et al. ∙ 0 ∙ share

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On the links between argumentation-based reasoning and nonmonotonic reasoning

In this paper we investigate the links between instantiated argumentatio...
01/13/2017 ∙ by Zimi Li, et al. ∙ 0 ∙ share

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PDDL+ Planning via Constraint Answer Set Programming

PDDL+ is an extension of PDDL that enables modelling planning domains wi...
08/31/2016 ∙ by Marcello Balduccini, et al. ∙ 0 ∙ share

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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...
02/20/2017 ∙ by Adriano Barra, et al. ∙ 0 ∙ share

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Random Forest regression for manifold-valued responses

An increasing array of biomedical and computer vision applications requi...
01/29/2017 ∙ by Dimosthenis Tsagkrasoulis, et al. ∙ 0 ∙ share

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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...
01/23/2017 ∙ by Petros-Pavlos Ypsilantis, et al. ∙ 0 ∙ share

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A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

Inspired by recent advances in deep learning, we propose a framework for...
04/08/2017 ∙ by Jo Schlemper, et al. ∙ 0 ∙ share

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Gaussian Processes for Survival Analysis

We introduce a semi-parametric Bayesian model for survival analysis. The...
11/02/2016 ∙ by Tamara Fernández, et al. ∙ 0 ∙ share

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Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks

Motivated by the need to automate medical information extraction from fr...
09/27/2016 ∙ by Savelie Cornegruta, et al. ∙ 0 ∙ share

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Deep metric learning for multi-labelled radiographs

Many radiological studies can reveal the presence of several co-existing...
12/11/2017 ∙ by Mauro Annarumma, et al. ∙ 0 ∙ share

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Theory of Semi-Instantiation in Abstract Argumentation

We study instantiated abstract argumentation frames of the form (S,R,I),...
04/27/2015 ∙ by D. M. Gabbay, et al. ∙ 0 ∙ share

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Probabilistic Argumentation. An Equational Approach

There is a generic way to add any new feature to a system. It involves 1...
03/18/2015 ∙ by D. M. Gabbay, et al. ∙ 0 ∙ share

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The Computational Complexity of Structure-Based Causality

Halpern and Pearl introduced a definition of actual causality; Eiter and...
12/09/2014 ∙ by Gadi Aleksandrowicz, et al. ∙ 0 ∙ share

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Equilibrium States in Numerical Argumentation Networks

Given an argumentation network with initial values to the arguments, we ...
08/28/2014 ∙ by D. Gabbay, et al. ∙ 0 ∙ share

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Abduction and Dialogical Proof in Argumentation and Logic Programming

We develop a model of abduction in abstract argumentation, where changes...
07/15/2014 ∙ by Richard Booth, et al. ∙ 0 ∙ share

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Learning population and subject-specific brain connectivity networks via Mixed Neighborhood Selection

In neuroimaging data analysis, Gaussian graphical models are often used ...
12/07/2015 ∙ by Ricardo Pio Monti, et al. ∙ 0 ∙ share

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Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks

Pattern recognition methods using neuroimaging data for the diagnosis of...
02/09/2015 ∙ by Adrien Payan, et al. ∙ 0 ∙ share

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