Pre Trained Segmentation Model

Pre-trained segmentation models leverage the power of large datasets and transfer learning to improve the efficiency and accuracy of image segmentation across diverse applications. Current research focuses on adapting these models to new domains (e.g., different medical imaging modalities, satellite imagery) using techniques like domain adaptation, prompt learning, and test-time adaptation, often employing architectures such as U-Net, Swin UNETR, and transformers. This work is significant because it addresses the limitations of training segmentation models from scratch, particularly in data-scarce scenarios, leading to improved performance in various fields including medical imaging, autonomous driving, and remote sensing.