PI-NAIM: Path-Integrated Neural Adaptive Imputation Model

TL;DR

PI-NAIM introduces a dual-path neural architecture that adaptively imputes missing data in medical imaging and multimodal settings, optimizing for accuracy and efficiency through intelligent routing and fusion.

Contribution

It presents a novel dual-path framework with dynamic routing and cross-path attention for improved imputation and downstream task performance.

Findings

Achieves state-of-the-art RMSE of 0.108 on MIMIC-III.

Substantial improvements in downstream AUROC for mortality prediction.

Modular design adaptable to various incomplete data scenarios.

Abstract

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