Bayesian neural network predicts lung tumor growth with uncertainty

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Researchers have developed a Bayesian physics-informed neural network to predict lung tumor growth using sparse longitudinal CT scan data. This model combines Gompertz growth dynamics with Bayesian inference to estimate growth patterns and provide calibrated uncertainty intervals. Evaluated on data from the National Lung Screening Trial, the approach demonstrated accurate prediction and uncertainty estimation, suggesting its utility for tumor growth assessment with limited follow-up scans. AI

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IMPACT Offers a new method for uncertainty-aware medical prognostics, potentially improving patient care with limited data.

RANK_REASON Publication of an academic paper detailing a novel machine learning methodology. [lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 · Haoran Ma · 2026-05-13 14:00

Uncertainty-Aware Prediction of Lung Tumor Growth from Sparse Longitudinal CT Data via Bayesian Physics-Informed Neural Networks

This work studies lung tumor growth prediction from sparse and irregular longitudinal computed tomography (CT) observations with measurement variability. A Bayesian physics-informed neural network is developed by combining Gompertz growth dynamics with low-dimensional Bayesian in…

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Uncertainty-Aware Prediction of Lung Tumor Growth from Sparse Longitudinal CT Data via Bayesian Physics-Informed Neural Networks

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