权重什么重要：通过标记重加权提升医学报告生成的样本效率
Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting
AI总结：
本文通过标记重加权方法提升医学报告生成的样本效率，实验表明在眼科报告生成中，该方法在较少训练数据下也能获得高质量报告。
摘要：
训练视觉语言模型（VLMs）进行医学报告生成常受到高质量标注数据稀缺的阻碍。本文评估了加权损失函数的使用以提高数据效率。与标准交叉熵损失相比，加权损失将重点放在语义显著且临床重要的标记上。在眼科报告生成实验中，我们展示该简单方法在多个数据规模下提升了效率，使用最多十倍较少的训练数据即可达到相似的报告质量。
英文摘要：
Training vision-language models (VLMs) for medical report generation is often hindered by the scarcity of high-quality annotated data. This work evaluates the use of a weighted loss function to improve data efficiency. Compared to standard cross-entropy loss, which treats all token prediction errors equally, the reweighted loss shifts the focus to semantically salient tokens with outsized clinical importance. In experiments on ophthalmological report generation, we show that this simple method improves efficiency across multiple data scales, achieving similar report quality with up to ten times less training data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
