RDAU-Net: Based on a Residual Convolutional Neural Network With DFP and CBAM for Brain Tumor Segmentation.

Due to the high heterogeneity of brain tumors, automatic segmentation of brain tumors remains a challenging task. In this paper, we propose RDAU-Net by adding dilated feature pyramid blocks with 3D CBAM blocks and inserting 3D CBAM blocks after skip-connection layers. Moreover, a CBAM with channel attention and spatial attention facilitates the combination of more expressive feature information, thereby leading to more efficient extraction of contextual information from images of various scales.

DFP-ResUNet:Convolutional Neural Network with a Dilated Convolutional Feature Pyramid for Multimodal Brain Tumor Segmentation.

Background And Objective: Manual brain tumor segmentation by radiologists is time consuming and subjective. Therefore, fully automatic segmentation of different brain tumor subregions is essential to the treatment of patients. In this paper, we propose a neural network for automatically segmenting the enhancing tumor (ET), whole tumor (WT), and tumor core (TC) brain tumor subregions.