Residual Channel Attention Network for Brain Glioma Segmentation

Autor:	Yao, Yiming, Qian, Peisheng, Zhao, Ziyuan, Zeng, Zeng
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Zdroj:	2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Druh dokumentu:	Working Paper
DOI:	10.1109/EMBC48229.2022.9871233
Popis:	A glioma is a malignant brain tumor that seriously affects cognitive functions and lowers patients' life quality. Segmentation of brain glioma is challenging because of interclass ambiguities in tumor regions. Recently, deep learning approaches have achieved outstanding performance in the automatic segmentation of brain glioma. However, existing algorithms fail to exploit channel-wise feature interdependence to select semantic attributes for glioma segmentation. In this study, we implement a novel deep neural network that integrates residual channel attention modules to calibrate intermediate features for glioma segmentation. The proposed channel attention mechanism adaptively weights feature channel-wise to optimize the latent representation of gliomas. We evaluate our method on the established dataset BraTS2017. Experimental results indicate the superiority of our method. Comment: Accepted by the 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2022)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.10758 Zobrazit plný text záznamu View this record from Arxiv