38 citations · 64 across the 5 of their papers we have counts for
5 papers
A self-supervised text-vision framework for automated brain abnormality detection
David A. Wood, Emily Guilhem, Sina Kafiabadi +13
Artificial neural networks trained on large, expert-labelled datasets are considered state-of-the-art for a range of medical image recognition tasks. However, categorically labelle…
Automated triaging of head MRI examinations using convolutional neural networks
David A. Wood, Sina Kafiabadi, Ayisha Al Busaidi +9
The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI…
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets
Reuben Dorent, Thomas Booth, Wenqi Li +5
Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been de…
Labelling imaging datasets on the basis of neuroradiology reports: a validation study
David A. Wood, Sina Kafiabadi, Aisha Al Busaidi +11
Natural language processing (NLP) shows promise as a means to automate the labelling of hospital-scale neuroradiology magnetic resonance imaging (MRI) datasets for computer vision…
Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)
David A. Wood, Jeremy Lynch, Sina Kafiabadi +13
Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a tr…