e-Health CSIRO at RRG24: Entropy-Augmented Self-Critical Sequence Training for Radiology Report Generation
This work addresses the need for assistive systems in radiology to handle diverse report generation, though it is incremental as it builds on existing methods.
The paper tackled the problem of generating radiology reports from chest X-ray images by adding entropy regularization to self-critical sequence training, achieving first-place finishes in the RRG24 shared task.
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.