This study designs the dynamic fully connected layer (DyFC) to enhance the feature fusion, which redefines the weights and biases by adopting base vectors to represent the new weights and biases. The coefficients of the base vectors are learned based on each input feature, and the weights and biases are no longer shared but unique, which provides more directional expressiveness for each feature. In this study, a dual-stream mapping architecture model IUINet is proposed. IUINet combines the 3DShift operation and spatial separable convolution to achieve medical image segmentation tasks and maintain a balance between accuracy and efficiency. The proposed IUINet follows an encoder-decoder structure, where the encoder consists of two parts. One part includes the Shift operation and pointwise Conv1×1 operation, and the other part incorporates spatial separable convolution operation. IUINet utilizes multi-scale inputs and multi-scale feature mapping layers to improve the backpropagation speed and reduce the average backpropagation distance. Finally, this enhances the model accuracy, improves generalization ability, and reduces overfitting.