@Article{cmes.2023.027127,
AUTHOR = {Peng Geng, Ji Lu, Ying Zhang, Simin Ma, Zhanzhong Tang, Jianhua Liu},
TITLE = {TC-Fuse: A Transformers Fusing CNNs Network for Medical Image Segmentation},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {2},
PAGES = {2001--2023},
URL = {http://www.techscience.com/CMES/v137n2/53346},
ISSN = {1526-1506},
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 better than ordinary positional encoding. In the
other branch, traditional convolutional neural networks (CNNs) are used to extract different features of fewer
medical images. In addition, the attention fusion module BiFusion is used to effectively integrate the information
from the CNN branch and Transformer branch, and the fused features can effectively capture the global and local
context of the current spatial resolution. On the public standard datasets Gland Segmentation (GlaS), Colorectal
adenocarcinoma gland (CRAG) and COVID-19 CT Images Segmentation, the F1-score, Intersection over Union
(IoU) and parameters of the proposed TC-Fuse are superior to those by Axial Attention U-Net, U-Net, Medical
Transformer and other methods. And F1-score increased respectively by 2.99%, 3.42% and 3.95% compared with
Medical Transformer.},
DOI = {10.32604/cmes.2023.027127}
}