Medical Image Segmentation Algorithm Based on Self-attention and Multi-scale Input-Output
Refined fundus image segmentation results of diabetic retinopathy can better assist doctors in diagnosis.The appea-rance of large scale and high resolution segmentation data sets provides favorable conditions for more refined segmentation.The mainstream segmentation network based on U-Net,using convolution operation based on local operation,cannot fully excavate global information when making pixel prediction.The network model adopts single-input single-output structure,which makes it difficult to obtain multi-scale feature information.In order to maximize the use of existing large-scale high-resolution fundus image focal segmentation data sets and achieve more refined segmentation,better segmentation methods need to be designed.In this paper,U-Net is transformed based on the self-attention mechanism and multi-scale input/output structure,and a new seg-mentation network,SAM-Net,is proposed.The self-attention module is used to replace the traditional convolutional module,and the ability of the network to obtain global information is increased.The multi-scale input and multi-scale output structures are in-troduced to make it easier for the network to obtain multi-scale feature information.The image slicing method is used to reduce the input size of the model,so as to prevent the training difficulty of the neural network model from increasing due to the large pixel of the input picture.Finally,experimental results on IDRiD and FGADR data sets show that SAM-Net can achieve better performance than other methods.
万方数据
维普
