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arXiv Machine Learning By Yeonjae Jung, Minwoo Shin

Observation-Anchored Selective Assimilation for Longitudinal Tumor-State Proxy Forecasting in Post-Treatment Glioma

Read the original on arXiv Machine Learning →

The paper introduces Observation‑Anchored Selective Assimilation (OASA) for forecasting tumor‑state proxies in post‑treatment glioma patients using serial MRI observations. OASA anchors the patient‑specific state with an intermediate observation and selectively updates it via a tiered rule and voxel‑wise soft gate, outperforming baseline methods in Dice score at certain thresholds. The approach is validated on 120 patient triplets and the code is publicly released.

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