Automated spinal MRI labelling from reports using a large language model

Conference item

We propose a general pipeline to automate the extraction of labels from radiology reports using large language models, which we validate on spinal MRI reports. The efficacy of our method is measured on two distinct conditions: spinal cancer and stenosis. Using open-source models, our method surpasses GPT-4 on a held-out set of reports. Furthermore, we show that the extracted labels can be used to train an imaging model to classify the identified conditions in the accompanying MR scans. Both the cancer and stenosis classifiers trained using automated labels achieve comparable performance to models trained using scans manually annotated by clinicians. Code can be found at https://github.com/robinyjpark/AutoLabelClassifier.

Authors: Park, RY; Windsor, R; Jamaludin, A; Zisserman, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Host title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2024
• Publication date: 2024-10-04
• Acceptance date: 2024-06-17
• Event title: 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024)
• Event location: Marrakesh, Morocco
• Event website: https://conferences.miccai.org/2024/en/
• Event start date: 2024-10-06
• Event end date: 2024-10-10
• DOI: 10.1007/978-3-031-72086-4_10
• EISSN: 1611-3349
• ISSN: 0302-9743
• EISBN: 9783031720864
• ISBN: 9783031720857
• Language: English
• Keywords: radiological reports; metastasis; cancer; stenosis