  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.
