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Neural network-based approaches have taken the lead in medical image segmentation with the encoder-decoder architecture. However, these approaches are still limited to one neural structure, which is short in leveraging the strengths of the three dominant structures (Convolutional Neural Network, Transformer, and Multilayer Perceptron) simultaneously. Furthermore, simple skip connections cannot effectively bridge the semantic gap between the encoder and decoder at the same level. To alleviate the above problems, this paper proposes Mixed-Net,haode which cleverly formulates a strategy to synergize three neural network structures for medical image segmentation. Specifically, our method innovatively designs two components, namely a Semantic Gap Bridging Module (SGBM) and a Global Information Compensation Decoder (GICD). Convolution-based SGBM can validly expand the receptive field and …