Medical Landmark Detection

Medical landmark detection aims to automatically identify key anatomical points in medical images, improving diagnostic efficiency and accuracy. Current research focuses on developing robust and efficient methods, particularly using deep learning architectures like U-Nets and Vision Transformers, often incorporating techniques such as label augmentation, one-shot learning, and adaptive prompting to address data scarcity and improve generalization across diverse imaging modalities and anatomical regions. These advancements hold significant promise for streamlining clinical workflows, assisting in diagnosis and treatment planning, and ultimately improving patient care.