  U-Net and its extensions have achieved great success in medical image
segmentation. However, due to the inherent local characteristics of ordinary
convolution operations, U-Net encoder cannot effectively extract global context
information. In addition, simple skip connections cannot capture salient
features. In this work, we propose a fully convolutional segmentation network
(CMU-Net) which incorporates hybrid convolutions and multi-scale attention
gate. The ConvMixer module extracts global context information by mixing
features at distant spatial locations. Moreover, the multi-scale attention gate
emphasizes valuable features and achieves efficient skip connections. We
evaluate the proposed method using both breast ultrasound datasets and a
thyroid ultrasound image dataset; and CMU-Net achieves average Intersection
over Union (IoU) values of 73.27% and 84.75%, and F1 scores of 84.81% and
91.71%. The code is available at https://github.com/FengheTan9/CMU-Net.
