Automated Spinal MRI Labelling from Reports Using a Large Language Model
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910687624691712 |
|---|---|
| author | Park, Robin Y. Windsor, Rhydian Jamaludin, Amir Zisserman, Andrew |
| author_facet | Park, Robin Y. Windsor, Rhydian Jamaludin, Amir Zisserman, Andrew |
| contents | 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 labelling method is measured on five distinct conditions: spinal cancer, stenosis, spondylolisthesis, cauda equina compression and herniation. Using open-source models, our method equals or surpasses GPT-4 on a held-out set of reports. Furthermore, we show that the extracted labels can be used to train imaging models to classify the identified conditions in the accompanying MR scans. All 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17235 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Spinal MRI Labelling from Reports Using a Large Language Model Park, Robin Y. Windsor, Rhydian Jamaludin, Amir Zisserman, Andrew Image and Video Processing Computation and Language Computer Vision and Pattern Recognition 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 labelling method is measured on five distinct conditions: spinal cancer, stenosis, spondylolisthesis, cauda equina compression and herniation. Using open-source models, our method equals or surpasses GPT-4 on a held-out set of reports. Furthermore, we show that the extracted labels can be used to train imaging models to classify the identified conditions in the accompanying MR scans. All 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. |
| title | Automated Spinal MRI Labelling from Reports Using a Large Language Model |
| topic | Image and Video Processing Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.17235 |