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Bayesian neural network predicts lung tumor growth with uncertainty

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a Bayesian physics-informed neural network to predict lung tumor growth from sparse CT scan data. This model integrates Gompertz growth dynamics with Bayesian inference, using a two-stage approach for estimation. Evaluated on data from the National Lung Screening Trial, the framework demonstrated accurate predictions and provided calibrated uncertainty estimates, outperforming deterministic methods. AI

IMPACT This research offers a novel method for uncertainty-aware medical prognostics, potentially improving treatment planning with limited patient data.

RANK_REASON The cluster contains an academic paper detailing a new modeling approach for medical data.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Bayesian neural network predicts lung tumor growth with uncertainty

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Haoran Ma ·

    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…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    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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