通过重加权关键医学词汇，用更少数据实现高质量眼科学报告生成。
Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting
- 按临床重要性重加权预测误差，聚焦关键术语
- 仅需十分之一数据即可达到相近报告质量
- 适合数据稀缺的医疗AI场景，尤其眼科领域
训练视觉-语言模型（VLMs）用于医学报告生成常受限于高质量标注数据稀缺。本文评估了加权损失函数在提升数据效率方面的效果。与标准交叉熵损失（对所有词元预测错误同等对待）不同，重加权损失将关注点转向具有显著临床意义的语义关键词元。在眼科学报告生成任务中，实验表明该方法在多种数据规模下均能提升效率，使用最多减少十倍的训练数据即可达到相似报告质量。
原文摘要 · Abstract (English)
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.
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