  In recent years, computer-aided diagnosis has become an increasingly popular
topic. Methods based on convolutional neural networks have achieved good
performance in medical image segmentation and classification. Due to the
limitations of the convolution operation, the long-term spatial features are
often not accurately obtained. Hence, we propose a TransClaw U-Net network
structure, which combines the convolution operation with the transformer
operation in the encoding part. The convolution part is applied for extracting
the shallow spatial features to facilitate the recovery of the image resolution
after upsampling. The transformer part is used to encode the patches, and the
self-attention mechanism is used to obtain global information between
sequences. The decoding part retains the bottom upsampling structure for better
detail segmentation performance. The experimental results on Synapse
Multi-organ Segmentation Datasets show that the performance of TransClaw U-Net
is better than other network structures. The ablation experiments also prove
the generalization performance of TransClaw U-Net.
