Abstract
In recent years, significant progress has been made in the field of medical image segmentation through the application of deep learning and neural networks. Numerous studies have focused on optimizing encoders to extract more comprehensive key information. However, the importance of decoders in directly influencing the final output of images cannot be overstated. The ability of decoders to effectively leverage diverse information and further refine crucial details is of paramount importance. This paper proposes a medical image segmentation architecture named STCS-Net. The designed decoder in STCS-Net facilitates multi-scale filtering and correction of information from the encoder, thereby enhancing the accuracy of extracting vital features. Additionally, an information enhancement module is introduced in skip connections to highlight essential features and improve the inter-layer information interaction capabilities. Comprehensive evaluations on the ISIC2016, ISIC2018, and Lung datasets validate the superiority of STCS-Net across different scenarios. Experimental results demonstrate the outstanding performance of STCS-Net on all three datasets. Comparative experiments highlight the advantages of our proposed network in terms of accuracy and parameter efficiency. Ablation studies confirm the effectiveness of the introduced decoder and skip connection module. This research introduces a novel approach to the field of medical image segmentation, providing new perspectives and solutions for future developments in medical image processing and analysis.