Confidence-Gated Cloud-Edge Cascade Triage via Variational Risk Minimization for Medical Imaging
Read the original on arXiv Machine Learning →The paper introduces Variational Risk Minimization (VRM), a distillation framework that treats large vision‑language model (LVLM) report variants as Monte Carlo samples of latent clinical interpretations. VRM learns from a variationally marginalized teacher distribution, providing uncertainty‑aware supervision even when modalities are missing, and outperforms fine‑tuning baselines while improving calibration. In a compact edge‑student setup, a confidence‑gated cascade achieves an AUC of 0.941 at 103 ms latency with only 20.3 % cloud escalation, offering a clear reliability‑latency trade‑off for cloud‑edge medical imaging workflows.
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