Split learning (SL) has emerged as a promising approach for model training
without revealing the raw data samples from the data owners. However,
traditional SL inevitably leaks label privacy as the tail model (with the last
layers) should be placed on the server. To overcome this limitation, one
promising solution is to utilize U-shaped architecture to leave both early
layers and last layers on the user side. In this paper, we develop a novel
parallel U-shaped split learning and devise the optimal resource optimization
scheme to improve the performance of edge networks. In the proposed framework,
multiple users communicate with an edge server for SL. We analyze the
end-to-end delay of each client during the training process and design an
efficient resource allocation algorithm, called LSCRA, which finds the optimal
computing resource allocation and split layers. Our experimental results show
the effectiveness of LSCRA and that U-shaped PSL can achieve a similar
performance with other SL baselines while preserving label privacy. Index
Terms: U-shaped network, split learning, label privacy, resource allocation,
5G/6G edge networks.

本研究开发了一种新型的并行 U 形分割学习方法，并设计了最佳化资源分配方案，以提高边缘网络的性能。实验结果表明，该方案能够在保护标签隐私的同时，实现与其他分割学习基线模型类似的性能。

U 形并行分布学习的资源优化分配

Optimal Resource Allocation for U-Shaped Parallel Split Learning

The U-shaped architecture has emerged as a crucial paradigm in the design of
medical image segmentation networks. However, due to the inherent local
limitations of convolution, a fully convolutional segmentation network with
U-shaped architecture struggles to effectively extract global context
information, which is vital for the precise localization of lesions. While
hybrid architectures combining CNNs and Transformers can address these issues,
their application in real medical scenarios is limited due to the computational
resource constraints imposed by the environment and edge devices. In addition,
the convolutional inductive bias in lightweight networks adeptly fits the
scarce medical data, which is lacking in the Transformer based network. In
order to extract global context information while taking advantage of the
inductive bias, we propose CMUNeXt, an efficient fully convolutional
lightweight medical image segmentation network, which enables fast and accurate
auxiliary diagnosis in real scene scenarios. CMUNeXt leverages large kernel and
inverted bottleneck design to thoroughly mix distant spatial and location
information, efficiently extracting global context information. We also
introduce the Skip-Fusion block, designed to enable smooth skip-connections and
ensure ample feature fusion. Experimental results on multiple medical image
datasets demonstrate that CMUNeXt outperforms existing heavyweight and
lightweight medical image segmentation networks in terms of segmentation
performance, while offering a faster inference speed, lighter weights, and a
reduced computational cost. The code is available at
this https URL

提出了 CMUNeXt，这是一种高效的全卷积轻量级医学图像分割网络，能够提取全局上下文信息，实现快速准确的辅助诊断，并在多个医学图像数据集上表现出优越的分割性能。

CMUNeXt：一种基于大型卷积核和跳跃融合的高效医学图像分割网络

CMUNeXt: An Efficient Medical Image Segmentation Network based on Large  Kernel and Skip Fusion

Automated medical image segmentation can assist doctors to diagnose faster
and more accurate. Deep learning based models for medical image segmentation
have made great progress in recent years. However, the existing models fail to
effectively leverage Transformer and MLP for improving U-shaped architecture
efficiently. In addition, the multi-scale features of the MLP have not been
fully extracted in the bottleneck of U-shaped architecture. In this paper, we
propose an efficient U-shaped architecture based on Swin Transformer and
multi-scale MLP, namely STM-UNet. Specifically, the Swin Transformer block is
added to skip connection of STM-UNet in form of residual connection, which can
enhance the modeling ability of global features and long-range dependency.
Meanwhile, a novel PCAS-MLP with parallel convolution module is designed and
placed into the bottleneck of our architecture to contribute to the improvement
of segmentation performance. The experimental results on ISIC 2016 and ISIC
2018 demonstrate the effectiveness of our proposed method. Our method also
outperforms several state-of-the-art methods in terms of IoU and Dice. Our
method has achieved a better trade-off between high segmentation accuracy and
low model complexity.

本文提出了一种基于 Swin Transformer 和多尺度 MLP 的高效 U 形架构，即 STM-UNet，用于医学图像分割，旨在有效利用 Transformer 和 MLP 以提高模型性能和全局特征建模能力，设计了一种新型 PCAS-MLP 并将其放置到架构的 “瓶颈” 中，提高了分割性能。实验结果表明，我们提出的方法在 ISIC2016 和 ISIC2018 方面优于其他状态下最先进的方法，同时在高分割准确性和低模型复杂性之间取得了更好的平衡。