MK-UNet：用于医学图像分割的多核轻量级卷积神经网络
MK-UNet: Multi-kernel Lightweight CNN for Medical Image Segmentation
AI总结：
本文提出超轻量级多核U形网络MK-UNet，通过多核深度可分离卷积和多种注意力机制，以极低参数量和计算量在多个医学图像分割基准上超越现有方法。
摘要：
本文提出了MK-UNet，这是一种面向医学图像分割的超轻量级多核U形卷积神经网络的范式转变。MK-UNet的核心是我们设计的多核深度可分离卷积模块（MKDC），该模块能够通过多个卷积核灵活处理图像，同时捕获复杂的多分辨率空间关系。MK-UNet还通过精细的注意力机制突出图像的显著特征，包括通道注意力、空间注意力和分组门控注意力。我们的MK-UNet网络仅具有0.316M参数量和0.314G FLOPs的适度计算开销，不仅是一种非常轻量级的解决方案，而且在六个二值医学影像基准上实现了比最先进（SOTA）方法更高的分割精度。具体而言，MK-UNet在DICE分数上优于TransUNet，参数量和FLOPs分别减少了近333倍和123倍。类似地，与UNeXt相比，MK-UNet展现出更优的分割性能，在参数量减少4.7倍的情况下，DICE分数提升幅度最高达6.7%。我们的MK-UNet还以低得多的计算资源超越了其他近期轻量级网络，如MedT、CMUNeXt、EGE-UNet和Rolling-UNet。这一性能飞跃，加上计算开销的大幅降低，使MK-UNet成为在资源受限环境（如即时检测设备）中进行实时、高保真医学诊断的卓越解决方案。我们的实现代码可在https://github.com/SLDGroup/MK-UNet获取。
英文摘要：
In this paper, we introduce MK-UNet, a paradigm shift towards ultra-lightweight, multi-kernel U-shaped CNNs tailored for medical image segmentation. Central to MK-UNet is the multi-kernel depth-wise convolution block (MKDC) we design to adeptly process images through multiple kernels, while capturing complex multi-resolution spatial relationships. MK-UNet also emphasizes the images salient features through sophisticated attention mechanisms, including channel, spatial, and grouped gated attention. Our MK-UNet network, with a modest computational footprint of only 0.316M parameters and 0.314G FLOPs, represents not only a remarkably lightweight, but also significantly improved segmentation solution that provides higher accuracy over state-of-the-art (SOTA) methods across six binary medical imaging benchmarks. Specifically, MK-UNet outperforms TransUNet in DICE score with nearly 333$\times$ and 123$\times$ fewer parameters and FLOPs, respectively. Similarly, when compared against UNeXt, MK-UNet exhibits superior segmentation performance, improving the DICE score up to 6.7% margins while operating with 4.7$\times$ fewer #Params. Our MK-UNet also outperforms other recent lightweight networks, such as MedT, CMUNeXt, EGE-UNet, and Rolling-UNet, with much lower computational resources. This leap in performance, coupled with drastic computational gains, positions MK-UNet as an unparalleled solution for real-time, high-fidelity medical diagnostics in resource-limited settings, such as point-of-care devices. Our implementation is available at https://github.com/SLDGroup/MK-UNet.
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。
