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Found 4 papers, 1 papers with code
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.
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.
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.
