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Found 14 papers, 1 papers with code
Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors
Surface reconstruction is a vital tool in a wide range of areas of medical image analysis and clinical research.
Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders
Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health.
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction
Our key contribution is that the VAE disentangles the latent space based on `explanations' drawn from existing clinical knowledge.
Probabilistic 3D surface reconstruction from sparse MRI information
Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research.
A Multimodal Deep Learning Model for Cardiac Resynchronisation Therapy Response Prediction
Next, a multimodal deep learning classifier is used for CRT response prediction, which combines the latent spaces of the segmentation models of the two modalities.
Improved AI-based segmentation of apical and basal slices from clinical cine CMR
Using manual segmentations as a reference, CMR slices were assigned to one of four regions: non-cardiac, base, middle, and apex.
Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction
Evaluation of predictive deep learning (DL) models beyond conventional performance metrics has become increasingly important for applications in sensitive environments like healthcare.
AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography
In this work we propose for the first time an AI approach for deriving advanced biomarkers of systolic and diastolic LV function from 2-D echocardiography based on segmentations of the full cardiac cycle.
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 standard for cardiac function evaluation.
An AI tool for automated analysis of large-scale unstructured clinical cine CMR databases
We show that our proposed tool, which combines image pre-processing steps, a domain-generalisable AI algorithm trained on a large-scale multi-domain CMR dataset and quality control steps, allows robust analysis of (clinical or research) databases from multiple centres, vendors, and cardiac diseases.
Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging
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 cardiovascular function.
An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentation
However, the severity and nature of the bias varies between the models, highlighting the importance of model choice when attempting to train fair AI-based segmentation models for medical imaging tasks.
Bias in Unsupervised Anomaly Detection in Brain MRI
This work presents a novel analysis of biases in unsupervised anomaly detection.
Label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets With Domain Shift and Partial Labelling
A significant challenge faced when training with multiple diverse datasets is the variation in label presence, i. e. the combined data are often partially-labelled.
