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

• URL: http://arxiv.org/abs/2002.06588v1
• Date: Sun, 16 Feb 2020 15:04:52 GMT
• Title: Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)
• Authors: David A. Wood, Jeremy Lynch, Sina Kafiabadi, Emily Guilhem, Aisha Al Busaidi, Antanas Montvila, Thomas Varsavsky, Juveria Siddiqui, Naveen Gadapa, Matthew Townend, Martin Kiik, Keena Patel, Gareth Barker, Sebastian Ourselin, James H. Cole, Thomas C. Booth
• Abstract summary: We present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification. Our model's performance is comparable to that of an expert radiologist, and better than that of an expert physician. We make code available online for researchers to label their own MRI datasets for medical imaging applications.
• Score: 0.8163463207064016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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