Deep Neural Networks (DNNs) have become ubiquitous in medical image processing and analysis. Among them, U-Nets are very popular in various image segmentation tasks. Yet, little is known about how information flows through these networks and whether they are indeed properly designed for the tasks they are being proposed for. In this paper, we employ information-theoretic tools in order to gain insight into information flow through U-Nets. In particular, we show how mutual information between input/output and an intermediate layer can be a useful tool to understand information flow through various portions of a U-Net, assess its architectural efficiency, and even propose more efficient designs.

翻译：深神经网络(DNNS)在医学图像处理和分析中变得无处不在,其中U-Net在各种图像分割任务中非常受欢迎。然而,对于信息如何通过这些网络流动,以及它们是否确实为拟议任务设计得当,人们知之甚少。在本文中,我们使用信息理论工具来深入了解通过U-Net的信息流动。特别是,我们展示了输入/产出和中间层之间的相互信息如何成为了解通过U-Net各个部分的信息流动、评估其建筑效率、甚至提出更高效的设计的有用工具。