Few-shot Learning Approach for Differentiation of Atypical Parkinsonian Syndromes Using Susceptibility Weighted Imaging

Motivation: Recent research indicates that various atypical Parkinsonian syndromes (APSs) exhibit distinct and subtle patterns of iron accumulation in the globus pallidus and putamen, typically detected through susceptibility-weighted imaging (SWI). Goal(s): We propose a novel automated framework for distinguishing between APSs, specifically MSA-P and PSP, in SWI allowing the model to learn from a small amount of labeled data. Approach: We combined T1-weighted and SWI to create a Hybrid Contrast Image, facilitating precise registration. Furthermore, we used Hyperbolic Few-shot contrastive learning for similarity-based. Results: The model achieved a balanced accuracy of approximately 94.29%, demonstrating its superior robustness compared to other models and distance metrics. Impact: Our proposed approach demonstrated the potential to classify specific APS with high performance using a small amount of labeled data. Furthermore, it can be extended to apply not only to binary-classification of specific APS but also to the entire APS.