Automated spinal MRI labelling from reports using a large language model
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 surpasse...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Springer
2024
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| Summary: | 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. |
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