Residual Channel Attention Network for Brain Glioma Segmentation
This work addresses the challenge of segmenting brain gliomas for medical imaging applications, but it appears incremental as it builds on existing deep learning methods with a specific attention mechanism.
The authors tackled the problem of brain glioma segmentation by proposing a deep neural network with residual channel attention modules, achieving superior performance on the BraTS2017 dataset.
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.