2024/08/07 by Aaron Nicolson, Jinghui Liu, Nicolson, Aaron +7 · 3 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #AI in cancer detection #Biomedical Text Mining and Ontologies #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2408.03500
openalex publication_date 2024/08/07 · openalex created_date 2024/10/22 · openalex updated_date 2026/07/28
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.