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20182023
most citedUncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images

35 citations · 59 across the 18 of their papers we have counts for

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Showing 2018Show all

5 papers · 1 filter

cs.CV2018

Magnetic Resonance Fingerprinting using Recurrent Neural Networks

Ilkay Oksuz, Gastao Cruz, James Clough +6

Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single,…

cs.CV2018

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

Ilkay Oksuz, Bram Ruijsink, Esther Puyol-Anton +8

Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and…

cs.CV2018

Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

Ilkay Oksuz, Bram Ruijsink, Esther Puyol-Anton +6

Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as thos…

cs.CV2018

Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors

Katarína Tóthová, Sarah Parisot, Matthew C. H. Lee +5

Surface reconstruction is a vital tool in a wide range of areas of medical image analysis and clinical research. Despite the fact that many methods have proposed solutions to the r…

cs.CV2018

Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks

Matthew Sinclair, Christian F. Baumgartner, Jacqueline Matthew +9

Measurement of head biometrics from fetal ultrasonography images is of key importance in monitoring the healthy development of fetuses. However, the accurate measurement of relevan…