论文标题
不确定性：计算机断层扫描的端到端隐式神经表示的不确定性量化
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography
论文作者
论文摘要
隐式神经表示（INRS）为场景重建和计算机图形学取得了令人印象深刻的结果，在该场景重建和计算机图形上，它们的性能主要在重建精度上进行了评估。随着INR进入其他领域，模型预测为高风险的决策提供了信息，INR推理的不确定性量化变得至关重要。为此，我们在计算机断层扫描的背景下研究了INRS，INRS，不确定的贝叶斯重新制定，并根据准确性和校准评估了几种贝叶斯深度学习实现。我们发现它们达到了良好的不确定性，同时保持与其他经典，基于INR和基于CNN的重建技术的准确性竞争。与贝叶斯深度学习文献中的共同直觉相反，我们发现INR可以通过计算高效的蒙特卡洛辍学获得最佳校准，表现优于汉密尔顿蒙特卡洛和深层合奏。此外，与表现最佳的先验方法相反，Uncnestr不需要大型培训数据集，而仅需要少数几个验证图像。
Implicit neural representations (INRs) have achieved impressive results for scene reconstruction and computer graphics, where their performance has primarily been assessed on reconstruction accuracy. As INRs make their way into other domains, where model predictions inform high-stakes decision-making, uncertainty quantification of INR inference is becoming critical. To that end, we study a Bayesian reformulation of INRs, UncertaINR, in the context of computed tomography, and evaluate several Bayesian deep learning implementations in terms of accuracy and calibration. We find that they achieve well-calibrated uncertainty, while retaining accuracy competitive with other classical, INR-based, and CNN-based reconstruction techniques. Contrary to common intuition in the Bayesian deep learning literature, we find that INRs obtain the best calibration with computationally efficient Monte Carlo dropout, outperforming Hamiltonian Monte Carlo and deep ensembles. Moreover, in contrast to the best-performing prior approaches, UncertaINR does not require a large training dataset, but only a handful of validation images.
