Universal Lesion Detection

Universal lesion detection (ULD) in medical imaging aims to automatically identify various types of lesions across different organs from a single scan, improving diagnostic accuracy and efficiency. Current research focuses on overcoming challenges like incomplete annotations and class imbalance through techniques such as data augmentation, teacher-student models, self-paced curriculum learning, and anchor-free detection architectures, often incorporating transformer networks and multi-scale feature fusion. These advancements improve the sensitivity and precision of lesion detection, particularly for small lesions, ultimately assisting radiologists in cancer diagnosis and treatment planning.