轻量级视觉变换器架构，适配移动端医疗图像分割
EViT-Unet: U-Net Like Efficient Vision Transformer for Medical Image Segmentation on Mobile and Edge Devices
- 融合卷积与自注意力，构建高效U型网络
- 计算量显著降低，准确率仍保持高位
- 专为资源受限的医疗边缘设备设计
随着深度学习快速发展，基于卷积神经网络（CNN）的U型网络在医学图像分割中取得成功，并广泛应用于各类任务。然而，其捕捉全局特征能力有限，限制了在复杂分割任务中的表现。视觉变换器（ViT）的兴起有效弥补了CNN的这一缺陷，推动了基于ViT的U型网络在医学图像分割中的应用。但ViT的高计算需求使其难以部署于计算资源有限的医疗设备和移动平台。为此，我们提出EViT-UNet，一种高效的基于ViT的分割网络，在保持精度的同时显著降低计算复杂度，适用于资源受限的医疗设备。EViT-UNet采用U型架构，包含编码器、解码器、瓶颈层和跳跃连接，结合卷积操作与自注意力机制以优化效率。实验结果表明，EViT-UNet在医学图像分割中实现高精度，同时大幅降低计算开销。
原文摘要 · Abstract (English)
With the rapid development of deep learning, CNN-based U-shaped networks have succeeded in medical image segmentation and are widely applied for various tasks. However, their limitations in capturing global features hinder their performance in complex segmentation tasks. The rise of Vision Transformer (ViT) has effectively compensated for this deficiency of CNNs and promoted the application of ViT-based U-networks in medical image segmentation. However, the high computational demands of ViT make it unsuitable for many medical devices and mobile platforms with limited resources, restricting its deployment on resource-constrained and edge devices. To address this, we propose EViT-UNet, an efficient ViT-based segmentation network that reduces computational complexity while maintaining accuracy, making it ideal for resource-constrained medical devices. EViT-UNet is built on a U-shaped architecture, comprising an encoder, decoder, bottleneck layer, and skip connections, combining convolutional operations with self-attention mechanisms to optimize efficiency. Experimental results demonstrate that EViT-UNet achieves high accuracy in medical image segmentation while significantly reducing computational complexity.
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