让医学表格数据更聪明：用结构化语义提升模型表现
Unlocking the Power of Medical Tabular Data via Semantic-Aware Multimodal Pre-training
- 设计双维度表格建模框架，显式捕捉特征重要性与连续离散关系
- 在皮肤科和眼科数据集上达到新SOTA，跨域泛化能力强
- 适合医疗表结构数据建模、临床决策支持系统研发者
尽管视觉-语言模型在医学表示学习中占据主导地位，但非结构化文本缺乏结构化临床表格中固有的密集定量诊断表型。现有多模态预训练方法因语义无关的设计，将表格输入视为扁平向量，并采用不稳定的连续回归目标，未能充分挖掘其潜力。为此，我们提出一种新的语义感知框架，显式建模表格数据的内在二维结构。首先，针对不同特征间诊断重要性的层次关系，引入重要性感知自适应掩码，构建无需标签的课程学习策略，优先关注关键特征；其次，针对特征内部连续性与离散性的双重特性，提出软标签离散化模块，以稳定的分布匹配替代不稳定的数值回归，从而数学上保持序数关系。在大规模皮肤病学（SLICE-3D、HOP）和眼科学（EyePACS）数据集上的大量实验表明，该方法达到新的最先进水平（SOTA），展现出优异的鲁棒性和跨领域泛化能力。
原文摘要 · Abstract (English)
While vision-language models dominate medical representation learning, unstructured text lacks the dense, quantitative diagnostic phenotypes inherent in structured clinical tables. However, existing multimodal pre-training methods underutilize this potential due to semantic-agnostic designs that treat tabular inputs as flat vectors and employ unstable continuous regression objectives. To overcome this, we propose a novel semantic-aware framework explicitly modeling the intrinsic two-dimensional structure of tabular data. First, addressing the inter-feature hierarchy of varying diagnostic importance, we introduce Importance-Aware Adaptive Masking to construct a label-free curriculum prioritizing salient features. Second, addressing the intra-feature continuity-discreteness duality, we propose a Soft-Label Discretized Module that replaces unstable numerical regression with stable distribution matching, thereby mathematically preserving ordinal relationships. Extensive experiments across large-scale dermatology (SLICE-3D, HOP) and ophthalmology (EyePACS) datasets establish a new state-of-the-art (SOTA), demonstrating exceptional robustness and cross-domain generalizability.
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