TY - GEN
T1 - TCRNET
T2 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022
AU - Shan, Xinxin
AU - Ma, Tai
AU - Gu, Anqi
AU - Cai, Haibin
AU - Wen, Ying
N1 - Publisher Copyright:
© 2022 IEEE
PY - 2022
Y1 - 2022
N2 - Recently, several Transformer-based methods have been presented to improve image segmentation. However, since Transformer needs regular square images and has difficulty in obtaining local feature information, the performance of image segmentation is seriously affected. In this paper, we propose a novel encoder-decoder network named TCRNet, which makes Transformer, Convolutional neural network (CNN) and Recurrent neural network (RNN) complement each other. In the encoder, we extract and concatenate the feature maps from Transformer and CNN to effectively capture global and local feature information of images. Then in the decoder, we utilize convolutional RNN in the proposed recurrent decoding unit to refine the feature maps from the decoder for finer prediction. Experimental results on three medical datasets demonstrate that TCRNet effectively improves the segmentation precision.
AB - Recently, several Transformer-based methods have been presented to improve image segmentation. However, since Transformer needs regular square images and has difficulty in obtaining local feature information, the performance of image segmentation is seriously affected. In this paper, we propose a novel encoder-decoder network named TCRNet, which makes Transformer, Convolutional neural network (CNN) and Recurrent neural network (RNN) complement each other. In the encoder, we extract and concatenate the feature maps from Transformer and CNN to effectively capture global and local feature information of images. Then in the decoder, we utilize convolutional RNN in the proposed recurrent decoding unit to refine the feature maps from the decoder for finer prediction. Experimental results on three medical datasets demonstrate that TCRNet effectively improves the segmentation precision.
KW - Transformer-based method
KW - encoder-decoder network
KW - feature information
KW - image segmentation
UR - https://www.scopus.com/pages/publications/85134002338
U2 - 10.1109/ICASSP43922.2022.9747716
DO - 10.1109/ICASSP43922.2022.9747716
M3 - 会议稿件
AN - SCOPUS:85134002338
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2410
EP - 2414
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 May 2022 through 27 May 2022
ER -