21 citations · 30 across the 6 of their papers we have counts for
6 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…
Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysis
Siddharth Agarwal, David A. Wood, Mariusz Grzeda +5
Purpose: Most studies evaluating artificial intelligence (AI) models that detect abnormalities in neuroimaging are either tested on unrepresentative patient cohorts or are insuffic…
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…
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…
NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification
David Wood, James Cole, Thomas Booth
Deep learning is attracting significant interest in the neuroimaging community as a means to diagnose psychiatric and neurological disorders from structural magnetic resonance imag…