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    <abstract>The automation of radiology report generation (RRG) holds immense potential to alleviate radiologists’ workloads and improve diagnostic accuracy. Despite advancements in image captioning and vision-language pretraining, RRG remains challenging due to the lengthy and complex nature of radiology reports. In this work, we proposes the Divide and Conquer Radiology Report Generation (DCRRG) model, which breaks down full-text radiology reports into concise observation descriptions. This approach enables the model to capture fine-grained representations from each observation through a two-stage process: an encoding stage focusing on observation prediction tasks to learn fine-grained representations, and a decoding stage for integrating these descriptions into cohesive and comprehensive radiology reports. Experimental results on two benchmark datasets demonstrate that DCRRG achieves significant improvements across all evaluated metrics, underscoring its capability to generate semantically coherent and clinically accurate radiology reports.</abstract>
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%0 Conference Proceedings
%T Divide and Conquer Radiology Report Generation via Observation Level Fine-grained Pretraining and Prompt Tuning
%A Zhou, Yuanpin
%A Wang, Huogen
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhou-wang-2024-divide
%X The automation of radiology report generation (RRG) holds immense potential to alleviate radiologists’ workloads and improve diagnostic accuracy. Despite advancements in image captioning and vision-language pretraining, RRG remains challenging due to the lengthy and complex nature of radiology reports. In this work, we proposes the Divide and Conquer Radiology Report Generation (DCRRG) model, which breaks down full-text radiology reports into concise observation descriptions. This approach enables the model to capture fine-grained representations from each observation through a two-stage process: an encoding stage focusing on observation prediction tasks to learn fine-grained representations, and a decoding stage for integrating these descriptions into cohesive and comprehensive radiology reports. Experimental results on two benchmark datasets demonstrate that DCRRG achieves significant improvements across all evaluated metrics, underscoring its capability to generate semantically coherent and clinically accurate radiology reports.
%U https://aclanthology.org/2024.emnlp-main.433
%P 7597-7610

Markdown (Informal)

[Divide and Conquer Radiology Report Generation via Observation Level Fine-grained Pretraining and Prompt Tuning](https://aclanthology.org/2024.emnlp-main.433) (Zhou & Wang, EMNLP 2024)

• Divide and Conquer Radiology Report Generation via Observation Level Fine-grained Pretraining and Prompt Tuning (Zhou & Wang, EMNLP 2024)

ACL

• Yuanpin Zhou and Huogen Wang. 2024. Divide and Conquer Radiology Report Generation via Observation Level Fine-grained Pretraining and Prompt Tuning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 7597–7610, Miami, Florida, USA. Association for Computational Linguistics.