Work

Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning

Year: 2024

Type: article

Abstract: Differences in iron accumulation patterns have been observed in susceptibility-weighted images across different classes of atypical parkinsonian syndromes (APS). Deep learning methods have shown great potential in automatically detecting these differences. However, the models typically require extensively labeled training datasets, which are costly and pose patient privacy risks. To address the issue of limited training datasets, we propose a novel few-shot learning framework for classifying multiple system atrophy parkinsonian (MSA-P) and progressive supranuclear palsy (PSP) within the APS category using fewer data items. Our method identifies feature areas where iron accumulation patterns occur in classes other than the target classification (MSA-P vs. PSP) and enhances stability by leveraging a superior hyperbolic space embedding technique. Experimental results demonstrate significantly improved performance over conventional methods, as validated by ablation studies and visualizations. • Hybrid contrast (HC) combines SWI and T1w images for enhanced iron content detection. • Few-shot learning addresses limited medical datasets, enabling effective classification. • Hierarchical structures and hyperbolic methods improve classification accuracy.

Source: NeuroImage

DOI: 10.1016/j.neuroimage.2024.120940

Authors: Won June Choi, Jin HwangBo, Quan Anh Duong, Jae-Hyeok Lee, Jin Kyu Gahm

Institutions: Pusan National University, Pusan National University Yangsan Hospital

Language: English

Topic: Parkinson's Disease Mechanisms and Treatments

Subfield: Neurology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,450