@inproceedings{xiao-etal-2025-online,
title = "Online Iterative Self-Alignment for Radiology Report Generation",
author = "Xiao, Ting and
Shi, Lei and
Zhang, Yang and
Yang, HaoFeng and
Wang, Zhe and
Bai, Chenjia",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1348/",
doi = "10.18653/v1/2025.acl-long.1348",
pages = "27799--27814",
ISBN = "979-8-89176-251-0",
abstract = "Radiology Report Generation (RRG) is an important research topic for relieving radiologists' heavy workload. Existing RRG models mainly rely on supervised fine-tuning (SFT) based on different model architectures using data pairs of radiological images and corresponding radiologist-annotated reports. Recent research has shifted focus to post-training improvements, aligning RRG model outputs with human preferences using reinforcement learning (RL). However, the limited data coverage of high-quality annotated data poses risks of overfitting and generalization. This paper proposes a novel Online Iterative Self-Alignment (OISA) method for RRG that consists of four stages: self-generation of diverse data, self-evaluation for multi-objective preference data, self-alignment for multi-objective optimization and self-iteration for further improvement. Our approach allows for generating varied reports tailored to specific clinical objectives, enhancing the overall performance of the RRG model iteratively. Unlike existing methods, our framework significantly increases data quality and optimizes performance through iterative multi-objective optimization. Experimental results demonstrate that our method surpasses previous approaches, achieving state-of-the-art performance across multiple evaluation metrics."
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<abstract>Radiology Report Generation (RRG) is an important research topic for relieving radiologists’ heavy workload. Existing RRG models mainly rely on supervised fine-tuning (SFT) based on different model architectures using data pairs of radiological images and corresponding radiologist-annotated reports. Recent research has shifted focus to post-training improvements, aligning RRG model outputs with human preferences using reinforcement learning (RL). However, the limited data coverage of high-quality annotated data poses risks of overfitting and generalization. This paper proposes a novel Online Iterative Self-Alignment (OISA) method for RRG that consists of four stages: self-generation of diverse data, self-evaluation for multi-objective preference data, self-alignment for multi-objective optimization and self-iteration for further improvement. Our approach allows for generating varied reports tailored to specific clinical objectives, enhancing the overall performance of the RRG model iteratively. Unlike existing methods, our framework significantly increases data quality and optimizes performance through iterative multi-objective optimization. Experimental results demonstrate that our method surpasses previous approaches, achieving state-of-the-art performance across multiple evaluation metrics.</abstract>
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%0 Conference Proceedings
%T Online Iterative Self-Alignment for Radiology Report Generation
%A Xiao, Ting
%A Shi, Lei
%A Zhang, Yang
%A Yang, HaoFeng
%A Wang, Zhe
%A Bai, Chenjia
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F xiao-etal-2025-online
%X Radiology Report Generation (RRG) is an important research topic for relieving radiologists’ heavy workload. Existing RRG models mainly rely on supervised fine-tuning (SFT) based on different model architectures using data pairs of radiological images and corresponding radiologist-annotated reports. Recent research has shifted focus to post-training improvements, aligning RRG model outputs with human preferences using reinforcement learning (RL). However, the limited data coverage of high-quality annotated data poses risks of overfitting and generalization. This paper proposes a novel Online Iterative Self-Alignment (OISA) method for RRG that consists of four stages: self-generation of diverse data, self-evaluation for multi-objective preference data, self-alignment for multi-objective optimization and self-iteration for further improvement. Our approach allows for generating varied reports tailored to specific clinical objectives, enhancing the overall performance of the RRG model iteratively. Unlike existing methods, our framework significantly increases data quality and optimizes performance through iterative multi-objective optimization. Experimental results demonstrate that our method surpasses previous approaches, achieving state-of-the-art performance across multiple evaluation metrics.
%R 10.18653/v1/2025.acl-long.1348
%U https://aclanthology.org/2025.acl-long.1348/
%U https://doi.org/10.18653/v1/2025.acl-long.1348
%P 27799-27814
Markdown (Informal)
[Online Iterative Self-Alignment for Radiology Report Generation](https://aclanthology.org/2025.acl-long.1348/) (Xiao et al., ACL 2025)
ACL
- Ting Xiao, Lei Shi, Yang Zhang, HaoFeng Yang, Zhe Wang, and Chenjia Bai. 2025. Online Iterative Self-Alignment for Radiology Report Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 27799–27814, Vienna, Austria. Association for Computational Linguistics.