CDPM-Align: Multi-Scale Guidance-Aligned Diffusion Pretraining for Robust Few-Shot Anatomical Landmark Detection
2026-06-03 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors focus on finding key spots in medical images, which is important for helping doctors. They created a new method called CDPM-align that learns from a few images to better find these spots, especially when there aren't many labeled examples. By training their model to generate images in a smart way beforehand, they improved how well it predicts and how confident it is in those predictions. This approach could help make medical image analysis safer and more reliable.
Anatomical landmark detectionMedical image analysisRepresentation learningConditional diffusion modelsPre-trainingLow-annotation learningUncertainty estimationGenerative pre-trainingClinical deployment
Authors
Roberto Di Via, Irina Voiculescu, Francesca Odone, Vito Paolo Pastore
Abstract
Anatomical landmark detection is a fundamental task in medical image analysis supporting a wide range of diagnostic and interventional workflows. Although recent methods have achieved sub-millimetric localisation, accuracy alone is not sufficient for clinical deployment, requiring reliability and robustness in prediction. Despite its clinical relevance, the impact of representation learning in this context is still underexplored. In this work, we introduce CDPM-align, a multi-scale guidance-aligned conditional diffusion pre-training for anatomical landmark detection. Our experimental setup focuses on a few images and a few annotation regimes. Specifically, we employ three popular heterogeneous small-scale benchmark datasets for representation learning via conditional generative pre-training. Furthermore, we consider low-annotation scenarios for the downstream task of landmark detection, with 10 and 25 annotated images, reflecting realistic trade-offs between clinical effort and resource constraints for annotations. Our results confirm that generative pre-training enables the model to learn a robust representation. This improves both accuracy and uncertainty on the downstream tasks, advancing towards safe and efficient clinical deployment.