King's College London

research

∙ 03/09/2020

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,...

Fernando Pérez-García, et al. ∙

research

∙ 10/04/2019

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...

James R. Clough, et al. ∙

research

∙ 05/02/2022

A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis

Cine cardiac magnetic resonance (CMR) imaging is considered the gold sta...

Inês P. Machado, et al. ∙

research

∙ 03/23/2022

MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images

The lack of annotated datasets is a major challenge in training new task...

Andres Diaz-Pinto, et al. ∙

research

∙ 09/23/2019

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

Model-based approaches for image reconstruction, analysis and interpreta...

Daniel Rueckert, et al. ∙

research

∙ 09/24/2019

dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

AUTOMAP is a promising generalized reconstruction approach, however, it ...

Jo Schlemper, et al. ∙

research

∙ 05/21/2022

Transformer-based out-of-distribution detection for clinically safe segmentation

In a clinical setting it is essential that deployed image processing sys...

Mark S. Graham, et al. ∙

research

∙ 10/08/2019

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 ...

Jessica Dafflon, et al. ∙

research

∙ 07/13/2020

Active Crowd Counting with Limited Supervision

To learn a reliable people counter from crowd images, head center annota...

Zhen Zhao, et al. ∙

research

∙ 07/27/2021

A persistent homology-based topological loss for CNN-based multi-class segmentation of CMR

Multi-class segmentation of cardiac magnetic resonance (CMR) images seek...

Nick Byrne, et al. ∙

research

∙ 08/23/2019

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...

Nick Byrne, 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

∙ 09/08/2021

Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes

Stochastic processes are random variables with values in some space of p...

Cristopher Salvi, et al. ∙

research

∙ 09/25/2019

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

We present simple reconstruction networks for multi-coil data by extendi...

Jo Schlemper, et al. ∙

research

∙ 08/06/2018

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

Atrial Fibrillation (AF) is a common electro-physiological cardiac disor...

Nicolo' Savioli, et al. ∙

research

∙ 10/18/2021

Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative Adversarial Neural Networks

We are approaching a future where social robots will progressively becom...

Nguyen Tan Viet Tuyen, et al. ∙

research

∙ 09/06/2021

A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation

Quality control (QC) of MR images is essential to ensure that downstream...

Richard Shaw, et al. ∙

research

∙ 07/19/2018

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,...

Guotai Wang, et al. ∙

research

∙ 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 ...

Felix J. S. Bragman, et al. ∙

research

∙ 04/07/2022

Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces

Recent state-of-the-art performances of Vision Transformers (ViT) in com...

Simon Dahan, et al. ∙

research

∙ 02/13/2020

Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE

The increasing efficiency and compactness of deep learning architectures...

Petru-Daniel Tudosiu, et al. ∙

research

∙ 09/14/2021

Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP

A key part of the NLP ethics movement is responsible use of data, but ex...

Anna Rogers, et al. ∙

research

∙ 01/29/2019

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...

Zhana Kuncheva, et al. ∙

research

∙ 09/30/2021

Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion Extraction

Current laparoscopic camera motion automation relies on rule-based appro...

Martin Huber, et al. ∙

research

∙ 01/31/2020

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...

Esther Puyol-Antón, et al. ∙

research

∙ 01/12/2022

ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation

Automatic segmentation of lung lesions associated with COVID-19 in CT im...

Muhammad Asad, et al. ∙

research

∙ 08/06/2019

Explaining Deep Neural Networks Using Spectrum-Based Fault Localization

Deep neural networks (DNNs) increasingly replace traditionally developed...

Youcheng Sun, et al. ∙

research

∙ 01/12/2020

Channel Assignment in Uplink Wireless Communication using Machine Learning Approach

This letter investigates a channel assignment problem in uplink wireless...

Guangyu Jia, et al. ∙

research

∙ 10/30/2020

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...

Qingjie Meng, 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

∙ 01/12/2022

Adaptive Worker Grouping For Communication-Efficient and Straggler-Tolerant Distributed SGD

Wall-clock convergence time and communication load are key performance m...

Feng Zhu, et al. ∙

research

∙ 09/24/2018

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...

Kamran Zia, et al. ∙

research

∙ 10/18/2018

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

Automatic brain tumor segmentation plays an important role for diagnosis...

Guotai Wang, et al. ∙

research

∙ 12/21/2018

3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects

Extremely small objects (ESO) have become observable on clinical routine...

Carole H Sudre, et al. ∙

research

∙ 05/02/2020

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...

Christopher J Arthurs, et al. ∙

research

∙ 11/14/2021

Invariant Risk Minimisation for Cross-Organism Inference: Substituting Mouse Data for Human Data in Human Risk Factor Discovery

Human medical data can be challenging to obtain due to data privacy conc...

Odhran O'Donoghue, et al. ∙

research

∙ 09/17/2019

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...

Yining Wang, et al. ∙

research

∙ 09/03/2021

Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction

Hyperspectral imaging is one of the most promising techniques for intrao...

Peichao Li, 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

∙ 01/12/2022

Learning to Identify Top Elo Ratings: A Dueling Bandits Approach

The Elo rating system is widely adopted to evaluate the skills of (chess...

Xue Yan, et al. ∙

research

∙ 07/26/2022

Cross-Modality Image Registration using a Training-Time Privileged Third Modality

In this work, we consider the task of pairwise cross-modality image regi...

Qianye Yang, et al. ∙

research

∙ 06/01/2018

Oversegmenting Graphs

We propose a novel method to adapt a graph to image data. The method dri...

Alberto Gomez, et al. ∙

research

∙ 10/02/2018

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

We propose a model for tagging unstructured texts with an arbitrary numb...

Gaurav Singh, et al. ∙

research

∙ 06/14/2019

Global and Local Interpretability for Cardiac MRI Classification

Deep learning methods for classifying medical images have demonstrated i...

James R. Clough, et al. ∙

research

∙ 07/19/2018

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

Data augmentation has been widely used for training deep learning system...

Guotai Wang, et al. ∙

research

∙ 10/29/2018

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...

lkay Oksuz, et al. ∙

research

∙ 07/07/2019

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

Brain tissue segmentation from multimodal MRI is a key building block of...

Reuben Dorent, et al. ∙

research

∙ 07/25/2019

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

We propose a new deep learning method for tumour segmentation when deali...

Reuben Dorent, et al. ∙

research

∙ 09/26/2021

BioCopy: A Plug-And-Play Span Copy Mechanism in Seq2Seq Models

Copy mechanisms explicitly obtain unchanged tokens from the source (inpu...

YI LIU, et al. ∙

research

∙ 10/07/2021

Learning Pessimism for Robust and Efficient Off-Policy Reinforcement Learning

Popular off-policy deep reinforcement learning algorithms compensate for...

Edoardo Cetin, et al. ∙

King's College London

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