AI 中文总结
针对标签偏移与分块模态缺失的多模态域适应问题，提出参考锚定方法，结合代理标签辅助策略，在模拟与RCC应用中实现了分布偏移下的稳定预测。
AI 中文摘要
多模态域适应利用带标注的源数据集预测无标注目标总体的结果。在此场景中，不同源可能观测到不同的模态子集，且与目标存在分布偏移；用于对齐的跨模态模式依赖于结果分布，因此对齐未调整的源数据可能会对目标产生错误表征。然而，由于目标样本中无标注，无法直接测量结果的分布偏移，且缺失的模态块阻碍了简单的池化操作。我们提出一种参考锚定的域适应方法：利用所有源和目标均观测到的参考模态，在对齐前估计目标结果分布并重加权源观测值；随后，通过典型相关分析（CCA）将辅助模态映射到目标定义的公共表征，再通过岭回归映射在每个源中复现该表征；结果信息通过对齐表征上的密度比模型进行传递。当黄金标准源标签稀疏时，我们开发了代理标签辅助方法以实现鲁棒的域适应。我们建立了目标CCA与源岭回归映射的统一旋转匹配结果，以及目标条件结果分布的一致性。模拟实验与肾细胞癌（RCC）应用表明，该方法在分布偏移与分块模态缺失下，可提升校准度并实现稳定预测。
英文摘要
Multimodal domain adaptation uses labeled source datasets to predict outcomes in an unlabeled target population. Here, different sources may observe different subsets of modalities and have distribution shifts from the target. In this scenario, the cross-modal patterns used for alignment depend on the outcome distribution, so aligning unadjusted source data can misrepresent the target. Nevertheless, the distribution shift of the outcome cannot be measured directly when labels are unavailable in the target sample and the missing modality blocks prevent simple pooling. We propose a reference-anchored domain adaptation method. A reference modality observed in every source and the target is used to estimate the target outcome distribution and reweight source observations before alignment. The auxiliary modalities are then mapped to a common, target-defined representation obtained by canonical correlation analysis (CCA) in the target and reproduced in each source by ridge-regression maps. Outcome information is transferred through density-ratio models on the aligned representation. When gold-standard source labels are sparse, a surrogate-label-assisted approach is developed to enable robust domain adaptation. We establish a unified-rotation match-up result for the target CCA and source ridge maps and consistency of the target conditional outcome distribution. Simulations and a renal cell carcinoma (RCC) application show improved calibration and stable prediction under distributional shift and blockwise missing modalities.
Comments49 pages, 3 figures, 15 tables; includes supplementary material
