根据缺失复杂度自动选择最佳补全方法，提升医疗影像数据处理精度
PI-NAIM: Path-Integrated Neural Adaptive Imputation Model
- 双路径动态路由：简单缺失用统计法，复杂模式交由神经网络处理
- 融合双路输出，实现补全误差降低至0.108（基准0.119-0.152）
- 可直接接入视觉系统，适合处理缺模态、传感器异常等真实场景
医学影像与多模态临床场景常面临诊断流程中模态缺失问题。现有补全方法或表达能力不足，或计算成本过高。我们提出PI-NAIM，一种新型双路径架构，根据缺失复杂度动态分配样本至最优补全策略。该框架集成：(1) 智能路径路由，将低缺失度样本导向高效统计补全（MICE），复杂模式交由强大神经网络（带时间分析的GAIN）；(2) 跨路径注意力融合，利用缺失感知嵌入智能整合双分支输出；(3) 补全精度与下游任务性能端到端联合优化。在MIMIC-III及多模态基准上的大量实验表明，其表现达到当前最优水平，补全均方根误差为0.108（基线0.119–0.152），且在死亡预测任务中取得0.812的AUROC。PI-NAIM模块化设计可无缝集成于处理不完整传感器测量、缺失模态或损坏输入的视觉流水线，提供统一解决方案。代码已公开于https://github.com/AfifaKhaled/PI-NAIM-Path-Integrated-Neural-Adaptive-Imputation-Model。
原文摘要 · Abstract (English)
Medical imaging and multi-modal clinical settings often face the challange of missing modality in their diagnostic pipelines. Existing imputation methods either lack representational capacity or are computationally expensive. We propose PI-NAIM, a novel dual-path architecture that dynamically routes samples to optimized imputation approaches based on missingness complexity. Our framework integrates: (1) intelligent path routing that directs low missingness samples to efficient statistical imputation (MICE) and complex patterns to powerful neural networks (GAIN with temporal analysis); (2) cross-path attention fusion that leverages missingness-aware embeddings to intelligently combine both branches; and (3) end-to-end joint optimization of imputation accuracy and downstream task performance. Extensive experiments on MIMIC-III and multimodal benchmarks demonstrate state-of-the-art performance, achieving RMSE of 0.108 (vs. baselines' 0.119-0.152) and substantial gains in downstream tasks with an AUROC of 0.812 for mortality prediction. PI-NAIM's modular design enables seamless integration into vision pipelines handling incomplete sensor measurements, missing modalities, or corrupted inputs, providing a unified solution for real-world scenario. The code is publicly available at https://github.com/AfifaKhaled/PI-NAIM-Path-Integrated-Neural-Adaptive-Imputation-Model
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