Advancements in medical image segmentation: A review of transformer models
Advancements in medical image segmentation have been heavily driven by the integration of Transformer models, which address the limitations of Convolutional Neural Networks (CNNs) in capturing long-range dependencies and global context. While CNNs excel at local feature extraction, Transformers utilize self-attention mechanisms to model relationships between distant pixels, crucial for identifying complex anatomical structures. Key Advancements in Architectures Recent trends show a shift toward hybrid architectures, blending the local focus of CNNs with the global understanding of Transformers. Hybrid CNN-Transformer Models: These are currently the dominant approach, combining convolutional encoders for low-level details (edges, texture) with Transformer encoders for high-level semantic information. Key examples include TransUNet, which hybridizes Transformers and U-Net, and Swin-Unet, the first pure transformer-based U-shaped architecture using hierarchical attention. Hierarchical ...