TransUnet Model

TransUNet models represent a hybrid approach to medical image segmentation, combining the localized feature extraction strengths of U-Net architectures with the global contextual understanding provided by Transformers. Current research focuses on refining TransUNet architectures through enhancements like dual attention mechanisms, efficient decoder designs (e.g., sparse coding), and multi-task learning strategies to improve segmentation accuracy and efficiency across diverse medical imaging modalities (e.g., MRI, CT, ultrasound). These advancements are significantly impacting medical image analysis by improving the accuracy and speed of automated diagnosis and treatment planning, particularly for challenging tasks such as brain tumor and nuclei segmentation.