Peng Geng1, Ji Lu1, Ying Zhang2,*, Simin Ma1, Zhanzhong Tang2, Jianhua Liu3
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 2001-2023, 2023, DOI:10.32604/cmes.2023.027127
Abstract In medical image segmentation task, convolutional neural networks (CNNs) are difficult to capture long-range
dependencies, but transformers can model the long-range dependencies effectively. However, transformers have a
flexible structure and seldom assume the structural bias of input data, so it is difficult for transformers to learn
positional encoding of the medical images when using fewer images for training. To solve these problems, a
dual branch structure is proposed. In one branch, Mix-Feed-Forward Network (Mix-FFN) and axial attention are
adopted to capture long-range dependencies and keep the translation invariance of the model. Mix-FFN whose
depth-wise convolutions can provide position information is… More >