One Shot Landmark Detection

One-shot landmark detection aims to accurately identify key anatomical points in images using only a single labeled example per class, addressing the challenge of limited annotated data in many applications, particularly medical imaging. Recent research focuses on leveraging foundation models and self-supervised pre-training techniques, including diffusion models and contrastive learning, to improve accuracy and robustness, often incorporating architectural enhancements like dual-branch decoders or relative distance biases. This efficient approach holds significant promise for accelerating medical image analysis and other fields requiring rapid and data-efficient landmark identification, potentially improving diagnostic speed and accuracy.