Target Annotation

Target annotation, the process of labeling data for machine learning models, is crucial for training accurate and reliable systems across diverse fields like medical image analysis and automatic target recognition. Current research focuses on improving annotation efficiency and accuracy through techniques such as transfer learning (adapting models trained on one dataset to another), federated learning (training models on decentralized data), and contrastive learning (learning by comparing similar and dissimilar data points). These advancements are driving progress in areas such as medical diagnosis, radiotherapy planning, and object detection, ultimately leading to more robust and effective AI systems.