Annotation Region

Annotation region selection focuses on efficiently identifying and labeling the most informative parts of large datasets, minimizing manual effort while maximizing model performance. Current research emphasizes methods like prototype sampling and adaptive region selection, often incorporating self-supervised learning and uncertainty quantification to improve the quality and efficiency of annotation. These advancements are crucial for various applications, including medical image analysis (e.g., whole slide image segmentation) and 3D shape processing, where manual annotation is prohibitively expensive and time-consuming. Improved annotation strategies ultimately lead to more accurate and robust machine learning models across diverse scientific domains.