ArticleFrontiers in radiology2023
Factors affecting the labelling accuracy of brain MRI studies relevant for deep learning abnormality detection.
Article in Frontiers in radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Self-Supervised Text-Vision Alignment for Automated Brain MRI Abnormality Detection: A Multicenter Study (ALIGN Study).Radiology. Artificial intelligence · 2026Article
- Impact of hospital-specific domain adaptation on BERT-based models to classify neuroradiology reports.European radiology · 2025Article
- Two stage large language model approach enhancing entity classification and relationship mapping in radiology reports.Scientific reports · 2025Article
- Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses.Diagnostics (Basel, Switzerland) · 2025Review
- Optimising brain age estimation through transfer learning: A suite of pre-trained foundation models for improved performance and generalisability in a clinical setting.Human brain mapping · 2024Article
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Authors and funding
14 authors.
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Abstract
Unlocking the vast potential of deep learning-based computer vision classification systems necessitates large data sets for model training. Natural Language Processing (NLP)-involving automation of dataset labelling-represents a potential avenue to achieve this. However, many aspects of NLP for dataset labelling remain unvalidated. Expert radiologists manually labelled over 5,000 MRI head reports in order to develop a deep learning-based neuroradiology NLP report classifier. Our results demonstrate that binary labels (normal vs. abnormal) showed high rates of accuracy, even when only two MRI sequences (T2-weighted and those based on diffusion weighted imaging) were employed as opposed to all sequences in an examination. Meanwhile, the accuracy of more specific labelling for multiple disease categories was variable and dependent on the category. Finally, resultant model performance was shown to be dependent on the expertise of the original labeller, with worse performance seen with non-expert vs. expert labellers.
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