Search Results for author:
Found 8 papers, 1 papers with code
Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysis
Conclusion: The paucity of eligible studies reflects that most abnormality detection AI studies were not adequately validated in representative clinical cohorts.
A self-supervised text-vision framework for automated brain abnormality detection
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.
International environmental treaties: An honest or a misguided effort
Climate change and environmental concerns represent a global crisis accompanied by significant economic challenges.
NEURO HAND: A weakly supervised Hierarchical Attention Network for interpretable neuroimaging abnormality Detection
Clinical neuroimaging data is naturally hierarchical.
Automated triaging of head MRI examinations using convolutional neural networks
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.
Labelling imaging datasets on the basis of neuroradiology reports: a validation study
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 applications.
The gem5 Simulator: Version 20.0+
The open-source and community-supported gem5 simulator is one of the most popular tools for computer architecture research.
Hardware Architecture
Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)
Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications.
