  Transformer and its variants have been widely used for medical image
segmentation. However, the large number of parameter and computational load of
these models make them unsuitable for mobile health applications. To address
this issue, we propose a more efficient approach, the Efficient Group Enhanced
UNet (EGE-UNet). We incorporate a Group multi-axis Hadamard Product Attention
module (GHPA) and a Group Aggregation Bridge module (GAB) in a lightweight
manner. The GHPA groups input features and performs Hadamard Product Attention
mechanism (HPA) on different axes to extract pathological information from
diverse perspectives. The GAB effectively fuses multi-scale information by
grouping low-level features, high-level features, and a mask generated by the
decoder at each stage. Comprehensive experiments on the ISIC2017 and ISIC2018
datasets demonstrate that EGE-UNet outperforms existing state-of-the-art
methods. In short, compared to the TransFuse, our model achieves superior
segmentation performance while reducing parameter and computation costs by 494x
and 160x, respectively. Moreover, to our best knowledge, this is the first
model with a parameter count limited to just 50KB. Our code is available at
https://github.com/JCruan519/EGE-UNet.
