Adaptive Confidence-weighted Expansion for Trustworthy Multi-Omics Multimodal Fusion

2026-07-22

AI Summary

Existing multimodal fusion models often lack robustness under noisy or uninformative data and fail to dynamically assess data quality or produce reliable confidence estimates, limiting their applicability in high-stakes clinical settings. To address these challenges, this work proposes the Adaptive Confidence-weighted Extension (ACE) framework, which uniquely integrates intra-modality correlation–driven complementary modality generation with a dual-level dynamic confidence mechanism. This enables adaptive weighting of modality reliability and outputs a global trust score. Evaluated on four multi-omics datasets—BRCA, KIPAN, LGG, and ROSMAP—ACE significantly outperforms current methods, achieving notable improvements in both classification accuracy and confidence calibration, thereby enhancing model robustness and clinical trustworthiness.

Abstract

Multimodal learning is a robust approach to improve predictive performance in applications such as medical prognosis. However, the clinical applicability of models that use multimodal learning is hampered by their poor performance under noisy or uninformative data streams. Present fusion approaches often lack robust mechanisms for the dynamic assessment of data quality and for the provision of a trustable confidence score on the final prediction. This dissuades their deployment in safety-critical settings. To address these limitations, we introduce Adaptive Confidence-weighted Expansion (ACE), a novel framework to enhance the trustworthiness of multimodal fusion models. ACE first enhances the multimodal space by generating new, complementary modalities from intra-modality correlations. It then employs a dual-level confidence mechanism that (1) adaptively reweighs all modalities by their reliability before fusion and (2) estimates a global trust score over the fused, final decision. To evaluate ACE, we used four challenging multi-omics datasets (BRCA, KIPAN, LGG, and ROSMAP). ACE significantly outperforms existing state-of-the-art algorithms in both classification performance and confidence calibration. Our framework provides a more stable and robust data fusion method that facilitates the use of multimodal learning in addressing high-stakes problems.

Problem

• multimodal fusion
• data quality assessment
• confidence calibration
• trustworthy AI
• multi-omics

Innovation

• Adaptive Confidence-weighted Expansion
• multimodal fusion
• confidence calibration
• multi-omics
• trustworthy AI