论文标题
通过隐式内核进行医学成像的超互换
Hyper-Convolutions via Implicit Kernels for Medical Imaging
论文作者
论文摘要
卷积神经网络（CNN）是用于计算机视觉任务的最常用架构之一。 CNN的关键构建块是卷积内核，该内核汇总了从像素社区中的信息，并在所有像素上共享权重。标准CNN的容量及其性能直接与可学习的内核权重的数量直接相关，后者由通道数量和内核大小（支持）决定。在本文中，我们介绍了\ textit {Hyper-Convolution}，这是一个新颖的构建块，使用空间坐标隐式编码卷积内核。超互连将内核大小与可学习的参数的总数相结合，从而实现了更灵活的体系结构设计。我们在实验中证明，用超互音代替常规卷积可以通过更少的参数改善性能，并提高噪声的鲁棒性。我们在此处提供代码：\ emph {https://github.com/tym002/hyper-convolution}
The convolutional neural network (CNN) is one of the most commonly used architectures for computer vision tasks. The key building block of a CNN is the convolutional kernel that aggregates information from the pixel neighborhood and shares weights across all pixels. A standard CNN's capacity, and thus its performance, is directly related to the number of learnable kernel weights, which is determined by the number of channels and the kernel size (support). In this paper, we present the \textit{hyper-convolution}, a novel building block that implicitly encodes the convolutional kernel using spatial coordinates. Hyper-convolutions decouple kernel size from the total number of learnable parameters, enabling a more flexible architecture design. We demonstrate in our experiments that replacing regular convolutions with hyper-convolutions can improve performance with less parameters, and increase robustness against noise. We provide our code here: \emph{https://github.com/tym002/Hyper-Convolution}
