LRAE-Unet:a lightweight network for fully automatic segmentation of brain tumor from MRI
A lightweight residual attention enhanced Unet(LRAE-Unet)is designed for the fully automatic brain tumor segmentation.LRAE-Unet uses lightweight residual module to solve the problems of gradient disappearance and network degradation when the network layers increases,lightweight self-attention module to suppress the irrelevant areas and highlight the significant features of specific local areas,and enhanced average pooling module with a larger field of perception to reduce the space of feature map,save computing resources and avoid over-fitting.The experiment on BraTS 2019 dataset shows that the proposed method has a Dice similarity coefficient of 91.24%,88.64%and 88.32%in the segmentations of the whole tumor,tumor core and enhanced tumor,which proves its feasibility and effectiveness for brain tumor segmentation.
NETL
NSTL
万方数据
维普
