面向三维数据的尺度等变深度学习
Scale-Equivariant Deep Learning for 3D Data
AI总结：
本文提出三维尺度等变卷积层并构建尺度等变U-Net，用于医学图像分割，通过免除逐尺度学习提升三维CNN的性能与数据效率。
摘要：
卷积神经网络（CNNs）能够识别图像中任意位置的物体，这得益于卷积操作的平移等变性。群等变CNN将这种等变性推广到输入的其他变换。正确处理不同尺度的物体及其部件具有挑战性，尺度变化可能源于多种原因，例如物体本身的大小或成像模态的分辨率。在本文中，我们针对三维数据提出了一种尺度等变卷积网络层，以保证三维CNN中的尺度等变性。尺度等变性免去了必须分别学习每一种可能尺度的负担，使神经网络能够专注于更高层次的学习目标，从而带来更好的结果和更高的数据效率。我们概述了二维领域中尺度等变神经网络的理论基础和科学研究工作。随后，我们将二维中的概念迁移到三维空间，并构建了一个面向三维数据的尺度等变卷积层。利用所提出的尺度等变层，我们构建了一个用于医学图像分割的尺度等变U-Net，并将其与非尺度等变的基线方法进行比较。我们的实验证明了所提方法在实现三维医学图像分析的尺度等变性方面的有效性。我们在 https://github.com/wimmerth/scale-equivariant-3d-convnet 公开了代码，以供进一步的研究和应用。
英文摘要：
The ability of convolutional neural networks (CNNs) to recognize objects regardless of their position in the image is due to the translation-equivariance of the convolutional operation. Group-equivariant CNNs transfer this equivariance to other transformations of the input. Dealing appropriately with objects and object parts of different scale is challenging, and scale can vary for multiple reasons such as the underlying object size or the resolution of the imaging modality. In this paper, we propose a scale-equivariant convolutional network layer for three-dimensional data that guarantees scale-equivariance in 3D CNNs. Scale-equivariance lifts the burden of having to learn each possible scale separately, allowing the neural network to focus on higher-level learning goals, which leads to better results and better data-efficiency. We provide an overview of the theoretical foundations and scientific work on scale-equivariant neural networks in the two-dimensional domain. We then transfer the concepts from 2D to the three-dimensional space and create a scale-equivariant convolutional layer for 3D data. Using the proposed scale-equivariant layer, we create a scale-equivariant U-Net for medical image segmentation and compare it with a non-scale-equivariant baseline method. Our experiments demonstrate the effectiveness of the proposed method in achieving scale-equivariance for 3D medical image analysis. We publish our code at https://github.com/wimmerth/scale-equivariant-3d-convnet for further research and application.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
