STAMP: Stochastic MAE for Longitudinal Medical Images
Analysis
This paper introduces STAMP, a novel self-supervised learning approach (Siamese MAE) for longitudinal medical images. It addresses the limitations of existing methods in capturing temporal dynamics, particularly the inherent uncertainty in disease progression. The stochastic approach, conditioning on time differences, is a key innovation. The paper's significance lies in its potential to improve disease progression prediction, especially for conditions like AMD and Alzheimer's, where understanding temporal changes is crucial. The evaluation on multiple datasets and the comparison with existing methods further strengthens the paper's impact.
Key Takeaways
- •Proposes STAMP, a Siamese MAE framework for longitudinal medical images.
- •Employs a stochastic approach to capture temporal dynamics and uncertainty in disease progression.
- •Outperforms existing methods on AMD and Alzheimer's disease progression prediction.
- •Uses time difference between volumes as a conditioning factor.
“STAMP pretrained ViT models outperformed both existing temporal MAE methods and foundation models on different late stage Age-Related Macular Degeneration and Alzheimer's Disease progression prediction.”