Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning (2026.acl-long)
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| Challenge: | Recent reinforcement learning approaches have advanced radiology report generation (RRG) however, there are two limitations: report-level rewards offer limited evidence-grounded guidance for clinical faithfulness . |
| Approach: | They propose a method that uses group-wise evidence-aware alignment rewards and self-correcting preference learning to build a reliable, disease-agnostic preference dataset without human supervision. |
| Outcome: | ESC-RL promotes clinically faithful, disease-aligned reward and supports continual self-improvement during training. |
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| Challenge: | Existing methods for RRG rely on supervised fine-tuning based on data pairs of radiological images and corresponding radiologist-annotated reports. |
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| Challenge: | Existing reward models rely on scalar or pairwise judgments that fail to capture multifaceted nature of human preferences. |
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| Challenge: | Medical images are widely used in clinical decision-making, where writing radiology reports can be enhanced by automatic solutions to alleviate physicians’ workload. |
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Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai, Omar Almusa, Curtis Langlotz
| Challenge: | Neural image-to-text radiology report generation systems have been successful on NLG metrics, but they are not factually complete or consistent due to inadequate training and evaluation. |
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Kun Zhao, Chenghao Xiao, Sixing Yan, Haoteng Tang, William K. Cheung, Noura Al Moubayed, Liang Zhan, Chenghua Lin
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| Challenge: | Existing MLLMs are computationally expensive and may produce hallucinated content . RA-RRG uses large language models to generate radiology reports . |
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| Challenge: | Experimental results demonstrate the superiority of our approach to aligning large language models with human preferences. |
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