271 citations · 535 across the 37 of their papers we have counts for
26 papers · 1 filter
Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images
Tareen Dawood, Chen Chen, Baldeep S. Sidhua +9
Quantifying uncertainty of predictions has been identified as one way to develop more trustworthy artificial intelligence (AI) models beyond conventional reporting of performance m…
Bias in Unsupervised Anomaly Detection in Brain MRI
Cosmin I. Bercea, Esther Puyol-Antón, Benedikt Wiestler +3
Unsupervised anomaly detection methods offer a promising and flexible alternative to supervised approaches, holding the potential to revolutionize medical scan analysis and enhance…
An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentation
Tiarna Lee, Esther Puyol-Antón, Bram Ruijsink +3
In medical imaging, artificial intelligence (AI) is increasingly being used to automate routine tasks. However, these algorithms can exhibit and exacerbate biases which lead to dis…
Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views
Zhen Yuan, Esther Puyol-Anton, Haran Jogeesvaran +2
Abnormal spleen enlargement (splenomegaly) is regarded as a clinical indicator for a range of conditions, including liver disease, cancer and blood diseases. While spleen length me…
Addressing Deep Learning Model Calibration Using Evidential Neural Networks and Uncertainty-Aware Training
Tareen Dawood, Emily Chan, Reza Razavi +2
In terms of accuracy, deep learning (DL) models have had considerable success in classification problems for medical imaging applications. However, it is well-known that the output…
Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging
Emily Chan, Ciaran O'Hanlon, Carlota Asegurado Marquez +11
Flow analysis carried out using phase contrast cardiac magnetic resonance imaging (PC-CMR) enables the quantification of important parameters that are used in the assessment of car…
A systematic study of race and sex bias in CNN-based cardiac MR segmentation
Tiarna Lee, Esther Puyol-Anton, Bram Ruijsink +2
In computer vision there has been significant research interest in assessing potential demographic bias in deep learning models. One of the main causes of such bias is imbalance in…
Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions
Germain Morilhat, Naomi Kifle, Sandra FinesilverSmith +7
In many low-to-middle income (LMIC) countries, ultrasound is used for assessment of pleural effusion. Typically, the extent of the effusion is manually measured by a sonographer, l…
A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis
Inês P. Machado, Esther Puyol-Antón, Kerstin Hammernik +10
Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decade…
The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images
Devran Ugurlu, Esther Puyol-Anton, Bram Ruijsink +5
Domain shift refers to the difference in the data distribution of two datasets, normally between the training set and the test set for machine learning algorithms. Domain shift is…
Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction
Tareen Dawood, Chen Chen, Robin Andlauer +10
Evaluation of predictive deep learning (DL) models beyond conventional performance metrics has become increasingly important for applications in sensitive environments like healthc…
Improved AI-based segmentation of apical and basal slices from clinical cine CMR
Jorge Mariscal-Harana, Naomi Kifle, Reza Razavi +3
Current artificial intelligence (AI) algorithms for short-axis cardiac magnetic resonance (CMR) segmentation achieve human performance for slices situated in the middle of the hear…
Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data
Ines Machado, Esther Puyol-Anton, Kerstin Hammernik +8
Cine cardiac MRI is routinely acquired for the assessment of cardiac health, but the imaging process is slow and typically requires several breath-holds to acquire sufficient k-spa…
A Multimodal Deep Learning Model for Cardiac Resynchronisation Therapy Response Prediction
Esther Puyol-Antón, Baldeep S. Sidhu, Justin Gould +5
We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR)…
A persistent homology-based topological loss for CNN-based multi-class segmentation of CMR
Nick Byrne, James R Clough, Isra Valverde +2
Multi-class segmentation of cardiac magnetic resonance (CMR) images seeks a separation of data into anatomical components with known structure and configuration. The most popular C…
Channel Attention Networks for Robust MR Fingerprinting Matching
Refik Soyak, Ebru Navruz, Eda Ozgu Ersoy +5
Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times. The working principle of MRF relies on varyin…
Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients
Zhen Yuan, Esther Puyol-Anton, Haran Jogeesvaran +3
Sickle Cell Disease (SCD) is one of the most common genetic diseases in the world. Splenomegaly (abnormal enlargement of the spleen) is frequent among children with SCD. If left un…
Quality-aware semi-supervised learning for CMR segmentation
Bram Ruijsink, Esther Puyol-Anton, Ye Li +4
One of the challenges in developing deep learning algorithms for medical image segmentation is the scarcity of annotated training data. To overcome this limitation, data augmentati…
Left atrial ejection fraction estimation using SEGANet for fully automated segmentation of CINE MRI
Ana Lourenço, Eric Kerfoot, Connor Dibblin +7
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, characterised by a rapid and irregular electrical activation of the atria. Treatments for AF are often ine…
A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI
Nick Byrne, James R. Clough, Giovanni Montana +1
With respect to spatial overlap, CNN-based segmentation of short axis cardiovascular magnetic resonance (CMR) images has achieved a level of performance consistent with inter obser…
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction
Esther Puyol-Antón, Chen Chen, James R. Clough +9
Advances in deep learning (DL) have resulted in impressive accuracy in some medical image classification tasks, but often deep models lack interpretability. The ability of these mo…
Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with Bayesian inference for uncertainty-based quality-control
Esther Puyol Anton, Bram Ruijsink, Christian F. Baumgartner +4
Tissue characterisation with CMR parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadol…
Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation
Ilkay Oksuz, James R. Clough, Bram Ruijsink +6
Segmenting anatomical structures in medical images has been successfully addressed with deep learning methods for a range of applications. However, this success is heavily dependen…
Topology-preserving augmentation for CNN-based segmentation of congenital heart defects from 3D paediatric CMR
Nick Byrne, James R. Clough, Isra Valverde +2
Patient-specific 3D printing of congenital heart anatomy demands an accurate segmentation of the thin tissue interfaces which characterise these diagnoses. Even when a label set ha…