Natural language processing (NLP) in the medical domain can underperform in real-world applications involving small datasets in a non-English language with few labeled samples and imbalanced classes. There is yet no consensus on how to approach this problem. We evaluated a set of NLP models including BERT-like transformers, few-shot learning with sentence transformers (SetFit), and prompted large language models (LLM), using three datasets of radiology reports on magnetic resonance images of epilepsy patients in Danish, a low-resource language. Our results indicate that BERT-like models pretrained in the target domain of radiology reports currently offer the optimal performances for this scenario. Notably, the SetFit and LLM models underperformed compared to BERT-like models, with LLM performing the worst. Importantly, none of the models investigated was sufficiently accurate to allow for text classification without any supervision. However, they show potential for data filtering, which could reduce the amount of manual labeling required.

本研究针对小型不平衡数据集在非英语语言下的放射学文本分类问题，探讨了多种自然语言处理模型的性能，包括BERT及其变种、少量学习以及提示式大语言模型。研究发现，预训练于放射学报告的BERT模型在此场景下表现最佳，尽管所有模型仍需人工监督来提高分类精度，但它们在数据过滤方面显示出潜在价值。 

非英语语言中小型不平衡数据集的放射学文本分类