Bayesian PINN integrates Gompertz dynamics and HMC sampling to predict tumor growth from sparse CT data, achieving log-space RMSE of 0.20 with well-calibrated 95% credible intervals on 30 NLST patients.
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Uncertainty-Aware Prediction of Lung Tumor Growth from Sparse Longitudinal CT Data via Bayesian Physics-Informed Neural Networks
Bayesian PINN integrates Gompertz dynamics and HMC sampling to predict tumor growth from sparse CT data, achieving log-space RMSE of 0.20 with well-calibrated 95% credible intervals on 30 NLST patients.