Confidence-Gated Cloud-Edge Cascade Triage via Variational Risk Minimization for Medical Imaging
arXiv:2610.02269 · doi:10.1016/j.smhl.2026.100689
Abstract
Emergency chest X-ray (CXR) triage has a structural modality gap: reports arrive after triage decisions, yet multimodal foundation models require image-text inputs. We present Variational Risk Minimization (VRM), a distillation framework that treats LVLM-generated report variants as Monte Carlo samples of latent clinical interpretations. Rather than distilling from a single teacher target, VRM learns from a variationally marginalized teacher distribution, enabling uncertainty-aware supervision under missing-modality constraints. Under matched encoder families, VRM outperforms direct fine-tuning baselines and improves calibration with strong recovery from hallucinated supervision. Marginalized supervision reduces report-selection instability. In our compact edge-student instantiation, a confidence-gated cascade reaches AUC 0.941 at 103ms average latency with 20.3% cloud escalation, yielding an explicit reliability-latency operating point for cloud-edge clinical workflows.
14 pages, 6 figures, 14 tables. Accepted manuscript of the article published in Smart Health 41 (2026) 100689. Presented as an oral at IEEE/ACM CHASE 2026. Code: https://github.com/Yangxinyee/vrm-edge-triage