271 citations · 535 across the 37 of their papers we have counts for
11 papers · 1 filter
A Study of Demographic Bias in CNN-based Brain MR Segmentation
Stefanos Ioannou, Hana Chockler, Alexander Hammers +1
Convolutional neural networks (CNNs) are increasingly being used to automate the segmentation of brain structures in magnetic resonance (MR) images for research studies. In other a…
Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation
Esther Puyol-Anton, Bram Ruijsink, Stefan K. Piechnik +4
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…
Probabilistic 3D surface reconstruction from sparse MRI information
Katarína Tóthová, Sarah Parisot, Matthew Lee +4
Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research. A reliable and effective reconstruction tool shou…
A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology
James R. Clough, Nicholas Byrne, Ilkay Oksuz +3
We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly pro…
Explicit topological priors for deep-learning based image segmentation using persistent homology
James R. Clough, Ilkay Oksuz, Nicholas Byrne +2
We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so. Our meth…
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,…
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…
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…
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…
Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas
Esther Puyol-Anton, Bram Ruijsink, Helene Langet +4
The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and dis…
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…