论文标题
脑神经胶质瘤分割的残留通道注意网络
Residual Channel Attention Network for Brain Glioma Segmentation
论文作者
论文摘要
神经胶质瘤是一种恶性脑肿瘤，严重影响认知功能并降低患者的生活质量。由于肿瘤区域中的阶级歧义性，脑胶质瘤的分割具有挑战性。最近，深度学习方法在自动分割脑神经胶质瘤方面取得了出色的表现。但是，现有的算法无法利用与通道的相互依赖性来选择神经胶质瘤分割的语义属性。在这项研究中，我们实施了一个新型的深神经网络，该网络集成了残留的通道注意模块，以校准中间特征以进行神经胶质瘤分割。所提出的通道注意机制适应性地位在渠道方面具有优化神经胶质瘤的潜在表示。我们在已建立的数据集BRATS2017上评估我们的方法。实验结果表明我们方法的优越性。
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.
