A self-supervised text-vision framework for automated brain abnormality detection

no code implementations • 5 May 2024 • David A. Wood, Emily Guilhem, Sina Kafiabadi, Ayisha Al Busaidi, Kishan Dissanayake, Ahmed Hammam, Nina Mansoor, Matthew Townend, Siddharth Agarwal, Yiran Wei, Asif Mazumder, Gareth J. Barker, Peter Sasieni, Sebastien Ourselin, James H. Cole, Thomas C. Booth

To address these challenges, we present a self-supervised text-vision framework that learns to detect clinically relevant abnormalities in brain MRI scans by directly leveraging the rich information contained in accompanying free-text neuroradiology reports.

Anomaly Detection Language Modelling +1

Automated triaging of head MRI examinations using convolutional neural networks

no code implementations • 15 Jun 2021 • David A. Wood, Sina Kafiabadi, Ayisha Al Busaidi, Emily Guilhem, Antanas Montvila, Siddharth Agarwal, Jeremy Lynch, Matthew Townend, Gareth Barker, Sebastien Ourselin, James H. Cole, Thomas C. Booth

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 scans around the world.

Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021

no code implementations • 15 Apr 2021 • Thomas Booth, Bernice Akpinar, Andrei Roman, Haris Shuaib, Aysha Luis, Alysha Chelliah, Ayisha Al Busaidi, Ayesha Mirchandani, Burcu Alparslan, Nina Mansoor, Keyoumars Ashkan, Sebastien Ourselin, Marc Modat

The small numbers of patient included in studies, the high risk of bias and concerns of applicability in the study designs (particularly in relation to the reference standard and patient selection due to confounding), and the low level of evidence, suggest that limited conclusions can be drawn from the data.

BIG-bench Machine Learning Specificity