# Domain-adapted large language models for classifying nuclear medicine reports

arXiv: https://arxiv.org/abs/2303.01258 · HTML: https://mlsanity.com/paper/2303.01258/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)
- [MMBERT: Multimodal BERT Pretraining for Improved Medical VQA](https://mlsanity.com/paper/2104.01394/) (2021)
- [Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation](https://mlsanity.com/paper/2109.12242/) (2021)

## Built on by (most cited first, 4 of 4)

- [Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT](https://mlsanity.com/paper/2304.04920/) (2023)
- [Taming Vision-Language Models for Medical Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2506.18378/) (2025)
- [Automatic Personalized Impression Generation for PET Reports Using Large Language Models](https://mlsanity.com/paper/2309.10066/) (2023)
- [A Specialized Large Multimodal Model for Interpreting PET/CT in Head and Neck Cancer](https://mlsanity.com/paper/2609.05532/) (2026)

## Abstract (arXiv)

With the growing use of transformer-based language models in medicine, it is unclear how well these models generalize to nuclear medicine which has domain-specific vocabulary and unique reporting styles. In this study, we evaluated the value of domain adaptation in nuclear medicine by adapting language models for the purpose of 5-point Deauville score prediction based on clinical 18F-fluorodeoxyglucose (FDG) PET/CT reports. We retrospectively retrieved 4542 text reports and 1664 images for FDG PET/CT lymphoma exams from 2008-2018 in our clinical imaging database. Deauville scores were removed from the reports and then the remaining text in the reports was used as the model input. Multiple general-purpose transformer language models were used to classify the reports into Deauville scores 1-5. We then adapted the models to the nuclear medicine domain using masked language modeling and assessed its impact on classification performance. The language models were compared against vision models, a multimodal vision language model, and a nuclear medicine physician with seven-fold Monte Carlo cross validation, reported are the mean and standard deviations. Domain adaption improved all language models. For example, BERT improved from 61.3% five-class accuracy to 65.7% following domain adaptation. The best performing model (domain-adapted RoBERTa) achieved a five-class accuracy of 77.4%, which was better than the physician's performance (66%), the best vision model's performance (48.1), and was similar to the multimodal model's performance (77.2). Domain adaptation improved the performance of large language models in interpreting nuclear medicine text reports.