Multi-Scale Orthogonal Model CNN–Transformer for Medical Image Segmentation

Abstract

Because of the limitations of convolution kernel, the traditional image segmentation network is not sufficient to obtain the context information, but the image segmentation task is very dependent on the context information. Transformer’s linear input can just get enough context information. In this paper, we propose a transformer segmentation network hyperfusion transformer based on a pyramid structure. First, the model divides the single-scale coding form into several-different-scale coding forms, and then fuses the decoding results. Second, in order to ensure the specificity of the output characteristics of each branch, we orthogonalize the results of a variety of different scales. By orthogonalizing in pairs, we can ensure that the results obtained by different branches are not completely similar to a certain extent, and reduce the redundancy of branch information. On the two datasets, the method in this paper surpasses a variety of classical models under multiple evaluation indexes, confirming that it is an effective segmentation method.