用视觉语言模型精准解析医疗文档，解决标注混乱难题
MeDocVL: A Visual Language Model for Medical Document Understanding and Parsing
- 通过训练驱动的标签优化提升噪声标注质量
- 在医疗发票数据集上超越主流OCR与视觉语言模型
- 适合需要高精度字段提取的医疗信息化场景
医疗文档光学字符识别因复杂版式、专业术语和噪声标注而具有挑战性，且要求字段级精确匹配。现有OCR系统和通用视觉语言模型难以可靠解析此类文档。我们提出MeDocVL，一种面向查询驱动的医疗文档解析的后训练视觉语言模型。框架结合训练驱动的标签精炼方法，从噪声标注中构建高质量监督信号；并采用噪声感知的混合后训练策略，融合强化学习与监督微调，实现鲁棒且精确的信息提取。在医疗发票基准测试中，MeDocVL持续优于传统OCR系统和强基准视觉语言模型，在噪声监督下达到当前最优性能。
原文摘要 · Abstract (English)
Medical document OCR is challenging due to complex layouts, domain-specific terminology, and noisy annotations, while requiring strict field-level exact matching. Existing OCR systems and general-purpose vision-language models often fail to reliably parse such documents. We propose MeDocVL, a post-trained vision-language model for query-driven medical document parsing. Our framework combines Training-driven Label Refinement to construct high-quality supervision from noisy annotations, with a Noise-aware Hybrid Post-training strategy that integrates reinforcement learning and supervised fine-tuning to achieve robust and precise extraction. Experiments on medical invoice benchmarks show that MeDocVL consistently outperforms conventional OCR systems and strong VLM baselines, achieving state-of-the-art performance under noisy supervision.
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