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Found 22 papers, 5 papers with code
Whole-examination AI estimation of fetal biometrics from 20-week ultrasound scans
We use a Bayesian method to estimate the true value of each biometric from a large number of measurements and probabilistically reject outliers.
Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images
The best-performing model in terms of both classification accuracy and the most common calibration measure, expected calibration error (ECE) was the Confidence Weight method, a novel approach that weights the loss of samples to explicitly penalise confident incorrect predictions.
Automatic retrieval of corresponding US views in longitudinal examinations
Skeletal muscle atrophy is a common occurrence in critically ill patients in the intensive care unit (ICU) who spend long periods in bed.
Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis
So far, video generation has only been possible by providing input data that is as rich as the output data, e. g., image sequence plus conditioning in, video out.
Addressing Deep Learning Model Calibration Using Evidential Neural Networks and Uncertainty-Aware Training
However, in our complex artefact detection task, we saw an improvement in calibration for both a low and higher-capacity model when implementing both the ENN and uncertainty-aware training together, indicating that this approach can offer a promising way to improve calibration in such settings.
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 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.
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.
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.
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.
Can non-specialists provide high quality gold standard labels in challenging modalities?
We study the time and cost implications of using novice annotators, the raw performance of novice annotators compared to gold-standard expert annotators, and the downstream effects on a trained Deep Learning segmentation model's performance for detecting a specific congenital heart disease (hypoplastic left heart syndrome) in fetal ultrasound imaging.
B-line Detection in Lung Ultrasound Videos: Cartesian vs Polar Representation
As a result, we hypothesize that a polar representation may be more adequate for automatic image analysis of these images.
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts.
Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation
The subject of "fairness" in artificial intelligence (AI) refers to assessing AI algorithms for potential bias based on demographic characteristics such as race and gender, and the development of algorithms to address this bias.
Automatic Detection of B-lines in Lung Ultrasound Videos From Severe Dengue Patients
Lung ultrasound (LUS) imaging is used to assess lung abnormalities, including the presence of B-line artefacts due to fluid leakage into the lungs caused by a variety of diseases.
Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome
Congenital heart disease is considered as one the most common groups of congenital malformations which affects $6-11$ per $1000$ newborns.
Quality-aware semi-supervised learning for CMR segmentation
SemiQCSeg can be an efficient approach for training segmentation networks for medical image data when labelled datasets are scarce.
Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening
In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound.
Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with Bayesian inference for uncertainty-based quality-control
The PHiSeg network and QC were validated against manual analysis on a cohort of the UK Biobank containing healthy subjects and chronic cardiomyopathy patients.
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.
Fetal whole-heart 4D imaging using motion-corrected multi-planar real-time MRI
Expert evaluation suggested the reconstructed volumes can be used for comprehensive assessment of the fetal heart, either as an adjunct to ultrasound or in combination with other MRI techniques.
Medical Physics
Mind the gap: quantification of incomplete ablation patterns after pulmonary vein isolation using minimum path search
Pulmonary vein isolation (PVI) is a common procedure for the treatment of atrial fibrillation (AF).
Computational Engineering, Finance, and Science
