In the field of medical CT image processing, convolutional neural networks (CNNs) have been the dominant this http URL-decoder CNNs utilise locality for efficiency, but they cannot simulate distant pixel interactions this http URL research indicates that self-attention or transformer layers can be stacked to efficiently learn long-range this http URL constructing and processing picture patches as embeddings, transformers have been applied to computer vision applications. However, transformer-based architectures lack global semantic information interaction and require a large-scale training dataset, making it challenging to train with small data samples. In order to solve these challenges, we present a hierarchical contextattention transformer network (MHITNet) that combines the multi-scale, transformer, and hierarchical context extraction modules in skip-connections. The multi-scale module captures deeper CT semantic information, enabling transformers to encode feature maps of tokenized picture patches from various CNN stages as input attention sequences more effectively. The hierarchical context attention module augments global data and reweights pixels to capture semantic this http URL trials on three datasets show that the proposed MHITNet beats current best practises