In this paper, we introduce U-Net v2, a new robust and efficient U-Net variant for medical image segmentation. It aims to augment the infusion of semantic information into low-level features while simultaneously refining high-level features with finer details. For an input image, we begin by extracting multi-level features with a deep neural network encoder. Next, we enhance the feature map of each level by infusing semantic information from higher-level features and integrating finer details from lower-level features through Hadamard product. Our novel skip connections empower features of all the levels with enriched semantic characteristics and intricate details. The improved features are subsequently transmitted to the decoder for further processing and segmentation. Our method can be seamlessly integrated into any Encoder-Decoder network. We evaluate our method on several public medical image segmentation datasets for skin lesion segmentation and polyp segmentation, and the experimental results demonstrate the segmentation accuracy of our new method over state-of-the-art methods, while preserving memory and computational efficiency. Code is available at: https://github.com/yaoppeng/U-Net\_v2

翻译：本文提出了一种新的鲁棒且高效的U-Net变体——U-Net v2，用于医学图像分割。其目标是在增强低级特征中语义信息注入的同时，利用更精细的细节对高级特征进行优化。对于输入图像，我们首先通过深度神经网络编码器提取多层级特征，随后利用高阶特征的语义信息与低阶特征中的细节信息，通过Hadamard积增强每一层的特征图。我们提出的新型跳跃连接使所有层级的特征均具备丰富的语义特征与精细的细节信息。改进后的特征随后被传输至解码器进行进一步处理与分割。我们的方法可无缝集成至任意编码器-解码器网络。我们在多个公开的医学图像分割数据集（皮肤病变分割与息肉分割）上评估了该方法，实验结果表明，相较于现有最优方法，我们的新方法在保持内存与计算效率的同时，取得了更优的分割精度。代码见：https://github.com/yaoppeng/U-Net\_v2