ArticleRadiology. Artificial intelligence2023
BERT-based Transfer Learning in Sentence-level Anatomic Classification of Free-Text Radiology Reports.
Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Automated Identification of Nursing Diagnoses and Interventions From Nursing Records Using a Retrieval-Augmented Large Language Model Approach: Quantitative Study.Journal of medical Internet research · 2026Article
- ModernBERT is more efficient than conventional BERT for chest CT findings classification in Japanese radiology reports.Scientific reports · 2026Article
- Context-Aware Filtering of Unstructured Radiology Reports by Anatomical Region.Proceedings of machine learning research · 2026Article
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- Leveraging Hybrid Natural Language Processing Techniques for Large-Scale Pulmonary Embolism Identification.JACC. Advances · 2025Article
- Development of a Large-Scale Dataset of Chest Computed Tomography Reports in Japanese and a High-Performance Finding Classification Model: Dataset Development and Validation Study.JMIR medical informatics · 2025Article
- The Evolution of Radiology Image Annotation in the Era of Large Language Models.Radiology. Artificial intelligence · 2025Review
- A Large Language Model to Detect Negated Expressions in Radiology Reports.Journal of imaging informatics in medicine · 2025Article
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- Data transformation of unstructured electroencephalography reports by natural language processing: improving data usability for large-scale epilepsy studies.Frontiers in neurology · 2025Article
- Uncertainty-aware automatic TNM staging classification for [BMC medical informatics and decision making · 2024Article
- Comparison of natural language processing algorithms in assessing the importance of head computed tomography reports written in Japanese.Japanese journal of radiology · 2024Article
- Evaluating the performance of Generative Pre-trained Transformer-4 (GPT-4) in standardizing radiology reports.European radiology · 2024Article
- To BERT or not to BERT: advancing non-invasive prediction of tumor biomarkers using transformer-based natural language processing (NLP).European radiology · 2023Article
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8 authors.
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Abstract
Purpose: To assess whether transfer learning with a bidirectional encoder representations from transformers (BERT) model, pretrained on a clinical corpus, can perform sentence-level anatomic classification of free-text radiology reports, even for anatomic classes with few positive examples. Materials and Methods: This retrospective study included radiology reports of patients who underwent whole-body PET/CT imaging from December 2005 to December 2020. Each sentence in these reports (6272 sentences) was labeled by two annotators according to body part ("brain," "head & neck," "chest," "abdomen," "limbs," "spine," or "others"). The BERT-based transfer learning approach was compared with two baseline machine learning approaches: bidirectional long short-term memory (BiLSTM) and the count-based method. Area under the precision-recall curve (AUPRC) and area under the receiver operating characteristic curve (AUC) were computed for each approach, and AUCs were compared using the DeLong test. Results: The BERT-based approach achieved a macro-averaged AUPRC of 0.88 for classification, outperforming the baselines. AUC results for BERT were significantly higher than those of BiLSTM for all classes and those of the count-based method for the "brain," "chest," "abdomen," and "others" classes ( Conclusion: The BERT-based transfer learning approach outperformed the BiLSTM and count-based approaches in sentence-level anatomic classification of free-text radiology reports, even for anatomic classes with few labeled training data.
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