[HTML][HTML] Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …
respect to the quantity of high-performing solutions reported in the literature. End users are …
Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets
Every year, millions of brain MRI scans are acquired in hospitals, which is a figure
considerably larger than the size of any research dataset. Therefore, the ability to analyze …
considerably larger than the size of any research dataset. Therefore, the ability to analyze …
[HTML][HTML] Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping
Recent developments in artificial intelligence have generated increasing interest to deploy
automated image analysis for diagnostic imaging and large-scale clinical applications …
automated image analysis for diagnostic imaging and large-scale clinical applications …
Cardiac MRI segmentation with sparse annotations: ensembling deep learning uncertainty and shape priors
The performance of deep learning for cardiac magnetic resonance imaging (MRI)
segmentation is oftentimes degraded when using small datasets and sparse annotations for …
segmentation is oftentimes degraded when using small datasets and sparse annotations for …
[HTML][HTML] Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping
Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced
myocardial tissue characterisation. However, cardiac or respiratory motion can significantly …
myocardial tissue characterisation. However, cardiac or respiratory motion can significantly …
Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI
MJ Ankenbrand, L Shainberg, M Hock, D Lohr… - BMC Medical …, 2021 - Springer
Background Image segmentation is a common task in medical imaging eg, for volumetry
analysis in cardiac MRI. Artificial neural networks are used to automate this task with …
analysis in cardiac MRI. Artificial neural networks are used to automate this task with …
Estimating uncertainty in neural networks for cardiac MRI segmentation: a benchmark study
Objective: Convolutional neural networks (CNNs) have demonstrated promise in automated
cardiac magnetic resonance image segmentation. However, when using CNNs in a large …
cardiac magnetic resonance image segmentation. However, when using CNNs in a large …
Deep generative model-based quality control for cardiac MRI segmentation
In recent years, convolutional neural networks have demonstrated promising performance in
a variety of medical image segmentation tasks. However, when a trained segmentation …
a variety of medical image segmentation tasks. However, when a trained segmentation …
[HTML][HTML] Quality control-driven deep ensemble for accountable automated segmentation of cardiac magnetic resonance LGE and VNE images
Background Late gadolinium enhancement (LGE) cardiovascular magnetic resonance
(CMR) imaging is the gold standard for non-invasive myocardial tissue characterisation …
(CMR) imaging is the gold standard for non-invasive myocardial tissue characterisation …
STANet: Spatio-Temporal Adaptive Network and Clinical Prior Embedding Learning for 3D+ T CMR Segmentation
The segmentation of cardiac structure in magnetic resonance images (CMR) is paramount in
diagnosing and managing cardiovascular illnesses, given its 3D+ Time (3D+ T) sequence …
diagnosing and managing cardiovascular illnesses, given its 3D+ Time (3D+ T) sequence …