A recurrent positional encoding circular attention mechanism network for biomedical image segmentation

Deep-learning-based medical image segmentation techniques can assist doctors in disease diagnosis and rapid treatment. However, existing medical image segmentation models do not fully consider the dependence between feature segments in the feature extraction process, and the correlated features can be further extracted. Therefore, a recurrent positional encoding circular attention mechanism network (RPECAMNet) is proposed based on relative positional encoding for medical image segmentation. Multiple residual modules are used to extract the primary features of the medical images, which are thereafter converted into one-dimensional data for relative positional encoding. The recursive former is used to further extract features from medical images, and decoding is performed using deconvolution. An adaptive loss function is designed to train the model and achieve accurate medical-image segmentation. Finally, the proposed model is used to conduct comparative experiments on the synapse and self-constructed kidney datasets to verify the accuracy of the proposed model for medical image segmentation.