基于去噪分数匹配的基于分数的理想观察者近似用于精确已知信号的检测任务
Score-Based Ideal Observer Approximation via Denoising Score Matching for Signal-Known-Exactly Detection Tasks
- Wyant College of Optical Sciences, University of Arizona（亚利桑那大学怀恩特光学科学学院）
- University of Arizona College of Medicine – Tucson（亚利桑那大学图森医学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究针对精确已知信号的检测任务，提出基于分数的理想观察者SIO，用去噪卷积神经网络近似IO检验统计量，无需逐图采样或信号特定重训练，可紧密逼近IO性能。
AI中文摘要：
贝叶斯理想观察者（IO）为二元检测任务建立了任务性能的理论上限，但IO检验统计量的解析计算通常难以处理。基于马尔可夫链蒙特卡洛（MCMC）方法的数值方法，包括其近期基于深度生成模型的扩展，通常需要为每个测试图像进行大量后验采样。已有研究也探讨了用监督学习近似IO性能，但此类方法通常针对特定检测任务和信号进行训练，当任务或信号发生变化时可能需要重新训练。分数函数定义为对数概率密度的梯度，编码了数据分布的局部几何结构，是现代基于分数的生成建模中的基础量。本研究将IO检验统计量重新表述为分数函数的形式，提出了一种基于分数的理想观察者（SIO）。所提SIO使用仅在无信号图像上训练的去噪卷积神经网络来估计无信号的分数函数；训练完成后，得到的分数模型可用于近似涉及任意加性信号的检测任务的IO检验统计量，无需针对每个图像进行后验采样，也无需针对特定信号重新训练。数值研究采用了具有随机块状背景模型的精确已知信号（SKE）检测任务，结果表明，所提SIO能够紧密近似IO的性能。
英文摘要：
The Bayesian Ideal Observer (IO) establishes the theoretical upper bound on task performance for binary detection tasks. However, analytical computation of the IO test statistic is generally intractable. Numerical approaches based on Markov-chain Monte Carlo (MCMC) methods, including their recent deep generative model-based extensions, typically require extensive posterior sampling for each test image. Supervised learning has also been investigated to approximate the IO performance. However, such methods are typically trained for a specific detection task and signal and may require retraining when the task or signal changes. The score function, defined as the gradient of the log probability density, encodes the local geometry of the data distribution and is a fundamental quantity in modern score-based generative modeling. This work reformulates the IO test statistic in terms of the score function and introduces a score-based ideal observer (SIO). The proposed SIO uses a denoising convolutional neural network trained exclusively on signal-absent images to estimate the signal-absent score function. Once trained, the resulting score model can be used to approximate the IO test statistic for detection tasks involving arbitrary additive signals, without per-image posterior sampling or signal-specific retraining. Numerical studies consider a signal-known-exactly (SKE) detection task with a stochastic lumpy-background model. The results demonstrate that the proposed SIO can closely approximate the IO performance.
