Pixel Wise Annotated

Pixel-wise annotation, the process of labeling each pixel in an image with a specific class, is crucial for training accurate deep learning models for image segmentation tasks, particularly in medical imaging and object detection. Current research focuses on mitigating the high cost and time associated with this process by exploring weakly supervised and semi-supervised learning techniques, often employing models like U-Nets and Vision Transformers, along with innovative loss functions and data augmentation strategies to leverage limited annotated data effectively. These advancements are significantly impacting various fields by enabling the development of high-performing segmentation models with reduced reliance on extensive manual annotation, leading to more efficient and accessible applications in areas such as medical diagnosis and autonomous systems.