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.

中文翻译：

---

通过U网的信息流分析

深度神经网络（DNN）在医学图像处理和分析中无处不在。其中，U-Net在各种图像分割任务中非常受欢迎。然而，人们对信息如何流经这些网络以及它们是否确实针对要执行的任务进行了适当的设计知之甚少。在本文中，我们使用信息理论工具来深入了解通过U-Net进行的信息流。特别是，我们展示了输入/输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，评估其体系结构效率甚至提出更有效设计的有用工具。