Summary of Classification Of Radiological Text in Small and Imbalanced Datasets in a Non-english Language, by Vincent Beliveau et al.
Classification of Radiological Text in Small and Imbalanced Datasets in a Non-English Language
by Vincent Beliveau, Helene Kaas, Martin Prener, Claes N. Ladefoged, Desmond Elliott, Gitte M. Knudsen, Lars H. Pinborg, Melanie Ganz
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper explores the challenges of applying natural language processing (NLP) in medical settings, particularly when working with small datasets and low-resource languages like Danish. The authors evaluated several NLP models, including BERT-like transformers, few-shot learning with sentence transformers (SetFit), and prompted large language models (LLM), on three datasets of radiology reports. The results show that BERT-like models pretrained in the target domain offer the best performance for this scenario, while SetFit and LLM models underperformed. Although none of the models achieved sufficient accuracy for text classification without supervision, they demonstrate potential for data filtering to reduce manual labeling requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research paper is about how hard it can be to use computers to understand medical reports written in languages like Danish. The team tested different types of computer programs that analyze language and found that some do better than others at understanding these reports. They used three sets of data and discovered that the best programs are those that were trained on similar data before being tested. Although none of the programs was perfect, they could still help doctors and researchers by filtering out unnecessary information. |
Keywords
» Artificial intelligence » Bert » Few shot » Natural language processing » Nlp » Text classification
