e-Health CSIRO at RRG24: Entropy-Augmented Self-Critical Sequence Training for Radiology Report Generation
<|reference_start|>e-Health CSIRO at RRG24: Entropy-Augmented Self-Critical Sequence Training for Radiology Report Generation: The Shared Task on Large-Scale Radiology Report Generation (RRG24) aims to expedite the development of assistive systems for interpreting and reporting on chest X-ray (CXR) images. This task challenges participants to develop models that generate the findings and impression sections of radiology reports from CXRs from a patient's study, using five different datasets. This paper outlines the e-Health CSIRO team's approach, which achieved multiple first-place finishes in RRG24. The core novelty of our approach lies in the addition of entropy regularisation to self-critical sequence training, to maintain a higher entropy in the token distribution. This prevents overfitting to common phrases and ensures a broader exploration of the vocabulary during training, essential for handling the diversity of the radiology reports in the RRG24 datasets. Our model is available on Hugging Face https://huggingface.co/aehrc/cxrmate-rrg24.<|reference_end|>
@article{nicolson2024e-health,
    title={e-Health CSIRO at RRG24: Entropy-Augmented Self-Critical Sequence
  Training for Radiology Report Generation},
    author={Aaron Nicolson, Jinghui Liu, Jason Dowling, Anthony Nguyen, and Bevan
  Koopman},
    journal={arXiv preprint arXiv:2408.03500},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03500},
    primaryClass={cs.CV}
}