论文标题
贝叶斯的方法，用于量化临床医生在医疗图像量化中的可变性
Bayesian approaches for Quantifying Clinicians' Variability in Medical Image Quantification
论文作者
论文摘要
包括MRI，CT和超声在内的医学成像在临床决策中起着至关重要的作用。准确的分割对于测量图像感兴趣的结构至关重要。但是，手动分割是高度依赖性的，这导致了定量测量的高间隙和变异性。在本文中，我们探讨了通过深神经网络参数参数的贝叶斯预测分布可以捕获临床医生的内部变异性的可行性。通过探索和分析最近出现的近似推理方案，我们评估了近似贝叶斯深度学习是否具有分割后的后验可以学习分割和临床测量中的内部评估者变异性。实验以两种不同的成像方式进行：MRI和超声。我们从经验上证明，由深神经网络参数参数的贝叶斯预测分布可以近似临床医生的内部变异性。我们通过提供临床测量不确定性来定量分析医学图像，展示了一个新的观点。
Medical imaging, including MRI, CT, and Ultrasound, plays a vital role in clinical decisions. Accurate segmentation is essential to measure the structure of interest from the image. However, manual segmentation is highly operator-dependent, which leads to high inter and intra-variability of quantitative measurements. In this paper, we explore the feasibility that Bayesian predictive distribution parameterized by deep neural networks can capture the clinicians' inter-intra variability. By exploring and analyzing recently emerged approximate inference schemes, we evaluate whether approximate Bayesian deep learning with the posterior over segmentations can learn inter-intra rater variability both in segmentation and clinical measurements. The experiments are performed with two different imaging modalities: MRI and ultrasound. We empirically demonstrated that Bayesian predictive distribution parameterized by deep neural networks could approximate the clinicians' inter-intra variability. We show a new perspective in analyzing medical images quantitatively by providing clinical measurement uncertainty.
