提升卷积：利用高效MLP置换进行三维医学图像分割
Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation
AI总结：
提出PHNet混合网络，结合CNN与MLP，通过MLPP模块捕获长距离依赖并解决分辨率敏感性问题，在COVID-19-20和Synapse基准上以更低计算成本超越现有方法。
摘要：
近年来，视觉Transformer（ViT）的出现为3D数据集基准带来了显著进展，特别是在三维体积医学图像分割（Vol-MedSeg）领域。与此同时，多层感知器（MLP）网络因其与ViT相当的结果而重新受到研究者的青睐，尽管它排除了资源密集型的自注意力模块。在这项工作中，我们提出了一种用于Vol-MedSeg的新型可置换混合网络，命名为PHNet，它充分利用了卷积神经网络（CNN）和MLP两者的优势。PHNet通过采用2D和3D CNN的组合来提取局部特征，解决了3D体积数据固有的各向同性（isotropy）问题。此外，我们提出了一种高效的多层置换感知器（MLPP）模块，该模块在保留位置信息的同时捕获长距离依赖。这是通过轴分解操作实现的，该操作沿不同轴置换输入张量，从而能够分别编码位置信息。此外，MLPP通过令牌分割操作解决了Vol-MedSeg中MLP的分辨率敏感性问题，该操作将特征划分为更小的令牌并分别处理。大量实验结果验证了PHNet在广泛使用且具有挑战性的COVID-19-20和Synapse基准上，以较低的计算成本超越了最先进的方法。消融研究也证明了PHNet在利用CNN和MLP优势方面的有效性。代码可在Github上获取：\href{https://github.com/xiaofang007/PHNet}{https://github.com/xiaofang007/PHNet}。
英文摘要：
Recently, the advent of vision Transformer (ViT) has brought substantial advancements in 3D dataset benchmarks, particularly in 3D volumetric medical image segmentation (Vol-MedSeg). Concurrently, multi-layer perceptron (MLP) network has regained popularity among researchers due to their comparable results to ViT, albeit with the exclusion of the resource-intensive self-attention module. In this work, we propose a novel permutable hybrid network for Vol-MedSeg, named PHNet, which capitalizes on the strengths of both convolution neural networks (CNNs) and MLP. PHNet addresses the intrinsic isotropy problem of 3D volumetric data by employing a combination of 2D and 3D CNNs to extract local features. Besides, we propose an efficient multi-layer permute perceptron (MLPP) module that captures long-range dependence while preserving positional information. This is achieved through an axis decomposition operation that permutes the input tensor along different axes, thereby enabling the separate encoding of the positional information. Furthermore, MLPP tackles the resolution sensitivity issue of MLP in Vol-MedSeg with a token segmentation operation, which divides the feature into smaller tokens and processes them individually. Extensive experimental results validate that PHNet outperforms the state-of-the-art methods with lower computational costs on the widely-used yet challenging COVID-19-20 and Synapse benchmarks. The ablation study also demonstrates the effectiveness of PHNet in harnessing the strengths of both CNNs and MLP. The code is available on Github: \href{https://github.com/xiaofang007/PHNet}{https://github.com/xiaofang007/PHNet}.
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。
