Dermoscopic image segmentation based on Pyramid Residual Attention Module.

ABSTRACT: We propose a stacked convolutional neural network incorporating a novel and efficient pyramid residual attention (PRA) module for the task of automatic segmentation of dermoscopic images. Precise segmentation is a significant and challenging step for computer-aided diagnosis technology in skin lesion diagnosis and treatment. The proposed PRA has the following characteristics: First, we concentrate on three widely used modules in the PRA. The purpose of the pyramid structure is to extract the feature information of the lesion area at different scales, the residual means is aimed to ensure the efficiency of model training, and the attention mechanism is used to screen effective features maps. Thanks to the PRA, our network can still obtain precise boundary information that distinguishes heal

SUBMITTER: Jiang Y 

PROVIDER: S-EPMC9481037 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature