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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…
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…
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Samuel Budd, Matthew Sinclair, Thomas Day +11
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill requi…
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…