Multimodal deep learning for clinical decision support frequently fails in real-world deployments due to severe data missingness and sensor noise. Standard fusion and Mixture-of-Experts (MoE) architectures assume all modalities are uniformly informative, causing routing collapse and miscalibrated confidence when faced with corrupted inputs. My doctoral research addresses this fragility by shifting the paradigm from generative imputation to uncertainty-guided abstention. As a foundational step, I introduce QA-MoE (Quality-Aware MoE), which explicitly decouples reliability estimation from the routing process. By quantifying epistemic uncertainty, the model dynamically filters noisy modalities before fusion, ensuring sparse and stable inference. Building on this static robustness, my ongoing and future research extends to longitudinal and structured clinical data. I am developing PathFlow-EM, which integrates Expectation-Maximization to handle hidden variable distributions in unevenly sampled time-series, and Uncertainty-Guided Dynamic Topological Fusion (U-DTF), which uses topological data analysis to gate features based on evolving patient graphs. Ultimately, this research trajectory aims to deliver robust, calibration-aware architectures that form the computational basis for reliable Patient Digital Twins.
Lin-Peng Sun· Proceedings of the Thirty-Fi...· 0 citations
Clinical prediction increasingly relies on multi-modal inputs, where reliability and efficiency are crucial for real-world deployment. However, mainstream fusion and MoE gating typically treat all available modalities as uniformly beneficial and allow noisy or weakly informative modalities to perturb routing, leading to instability, routing collapse, and miscalibrated confidence under missingness and shift. We propose QA-MoE, a Quality-Aware and stable multimodal Mixture-of-Experts that decouples reliability estimation from routing to enable robust, sparse, and interpretable fusion. QA-MoE adopts a modular architecture where each modality is initially encoded into a shared embedding space. To deal with structurally missing data, we employ a completion pathway that maintains a consistent interface. Unlike standard approaches, QA-MoE separates reliability estimation from the routing process. We propose an Evidential Quality Scorer to measure epistemic uncertainty, which then guides a Stability-Enhanced Subset Selector to filter out noisy modalities on the fly. Additionally, we include a Ternary Expert Aggregation mechanism acting as a specialized branch to stabilize predictions when data missingness is severe. Evaluations on clinical benchmarks (ADNI for Alzheimer’s staging and MIMIC-IV for Length-of-Stay) demonstrate that QA-MoE outperforms strong multimodal baselines, improving reliability while cutting down unnecessary computation. This indicates that QA-MoE offers a robust solution for multimodal decision support, especially in clinical settings prone to noise and missing data.
Lin-Peng Sun, Victor S. Sheng· Proceedings of the Thirty-Fi...· 0 citations
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