Yingshu Li — accepted AI-conference papers

2026

ReFINE: A Reward-Based Framework for Interpretable and Nuanced Evaluation of Radiology Report Generation

Yunyi Liu, Yingshu Li, Zhanyu Wang, Xinyu Liang, Lingqiao Liu, Lei Wang +1

AAAI 2026technical

Automated radiology report generation (R2Gen) has advanced significantly, yet evaluation remains challenging due to the complexity of assessing report quality. Traditional metrics often misalign with human judgments, failing to identify specific deficiencies. To address this, we introduce ReFINE, a

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2026

SAT-RRG: LLM-Guided Self-Adaptive Training for Radiology Report Generation with Token-Level Push-Pull Optimization

Yunyi Liu, Yingshu Li, Tong Chen, Lingqiao Liu, Lei Wang, Luping Zhou

CVPR 2026

Radiology report generators often produce fluent text yet miss crucial details, leading to local semantic conflicts or flipped findings that require stronger penalties. **Cross-entropy (CE) merely increases the probability of the ground-truth token y^* without directly suppressing the model's curren

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