Residual Channel Attention Network for Brain Glioma Segmentation

TL;DR

This paper introduces a residual channel attention network that enhances brain glioma segmentation by adaptively weighting feature channels, leading to improved accuracy on the BraTS2017 dataset.

Contribution

The study proposes a novel neural network with residual channel attention modules to better exploit channel-wise feature interdependence for glioma segmentation.

Findings

Outperforms existing methods on BraTS2017 dataset

Effectively calibrates intermediate features for better segmentation

Demonstrates superior accuracy in glioma delineation

Abstract

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.