O089 - Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)

David Wood, Emily Guilhem, Antanas Montvila, Thomas Varsavsky, Martin Kiik, Juveria Siddiqui, Sina Kafiabadi, Naveen Gadapa, Aisha Al Busaidi, Matt Townend, Keena Patel, Gareth Barker, Sebastian Ourselin, Jeremy Lynch, James Cole, Tom Booth

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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 transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text expert radiology reports. Our model’s performance is comparable to that of an expert radiologist, and better than that of an expert physician, demonstrating the feasibility of this approach. We make code available online for researchers to label their own MRI datasets for medical imaging applications.
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Oral Session #6 - Attention - 12:30 - 13:30 UTC-4 (Wednesday)
Poster Session #6 - 13:30 - 15:00 UTC-4 (Wednesday)
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