  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
