Cervical cancer segmentation based on full-scale feature fusion with cascading-attention and dilated convolution

Yu Ding; Cong Sheng; Zhongyue Chen; Lingli Mao; Zhao Peng; Tingting Chen; Yiqun Liu; Wanli Huo

doi:10.1117/12.3003788

11 October 2023 Cervical cancer segmentation based on full-scale feature fusion with cascading-attention and dilated convolution

Yu Ding, Cong Sheng, Zhongyue Chen, Lingli Mao, Zhao Peng, Tingting Chen, Yiqun Liu, Wanli Huo

Author Affiliations +

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 1280033 (2023) https://doi.org/10.1117/12.3003788
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Cervical cancer is malignant tumors for women. It is crucial to delineate cervical cancer accurately from CT images. Existing tumor segmentation networks have problems in the cervical cancer segmentation process, such as insufficient feature extraction, poor universality, and low segmentation accuracy. We propose a Full-scale network combined with Cascading-attention and Dilated convolution (FCD- et). Firstly, we used transformer module to provide better global information extraction capabilities. Then, more detailed semantic information is obtained through the feature fusion module at the skip connection. Finally, we use dilated convolution and Squeeze-and-Extension network (SENet) to expand the receptive field, thereby extracting more characteristic information. Comparing the proposed method with mainstream methods, the experimental results show that our proposed method has significant advantages in various evaluation metrics and provide higher accuracy for cervical cancer segmentation.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yu Ding, Cong Sheng, Zhongyue Chen, Lingli Mao, Zhao Peng, Tingting Chen, Yiqun Liu, and Wanli Huo "Cervical cancer segmentation based on full-scale feature fusion with cascading-attention and dilated convolution", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 1280033 (11 October 2023); https://doi.org/10.1117/12.3003788

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