Dilated-UNet：一种使用扩张 Transformer 和 U-Net 架构的快速准确医学图像分割方法
Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net Architecture
AI总结：
本文提出 Dilated-UNet，将分层 Dilated Transformer 与 U-Net 结合，通过跳跃连接和局部、稀疏全局注意力实现快速准确的医学图像分割，并在 ISIC、Synapse 上取得优于其他模型的结果。
摘要：
医学图像分割对于计算机辅助诊断和治疗系统的开发至关重要，但仍面临诸多困难。近年来，常用的基于 CNN 的编码器-解码器架构已在医学图像分割中得到有效应用，但在学习全局上下文和空间关系方面存在局限。一些研究人员尝试将 transformer 同时融入解码器和编码器组件，并取得了有前景的结果，但由于其计算复杂度较高，这种方法仍需进一步改进。本文介绍了 Dilated-UNet，它将 Dilated Transformer 块与 U-Net 架构相结合，以实现准确且快速的医学图像分割。图像块被转换为 token，并输入到带有跳跃连接的 U 形编码器-解码器架构中，用于局部-全局语义特征学习。编码器采用分层 Dilated Transformer，结合 Neighborhood Attention 和 Dilated Neighborhood Attention Transformer，以提取局部和稀疏全局注意力。实验结果表明，Dilated-UNet 在 ISIC 和 Synapse 等多个具有挑战性的医学图像分割数据集上优于其他模型。
英文摘要：
Medical image segmentation is crucial for the development of computer-aided diagnostic and therapeutic systems, but still faces numerous difficulties. In recent years, the commonly used encoder-decoder architecture based on CNNs has been applied effectively in medical image segmentation, but has limitations in terms of learning global context and spatial relationships. Some researchers have attempted to incorporate transformers into both the decoder and encoder components, with promising results, but this approach still requires further improvement due to its high computational complexity. This paper introduces Dilated-UNet, which combines a Dilated Transformer block with the U-Net architecture for accurate and fast medical image segmentation. Image patches are transformed into tokens and fed into the U-shaped encoder-decoder architecture, with skip-connections for local-global semantic feature learning. The encoder uses a hierarchical Dilated Transformer with a combination of Neighborhood Attention and Dilated Neighborhood Attention Transformer to extract local and sparse global attention. The results of our experiments show that Dilated-UNet outperforms other models on several challenging medical image segmentation datasets, such as ISIC and Synapse.
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。
