Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning
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 exten...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811924004373 |
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| author | Won June Choi Jin HwangBo Quan Anh Duong Jae-Hyeok Lee Jin Kyu Gahm |
| author_facet | Won June Choi Jin HwangBo Quan Anh Duong Jae-Hyeok Lee Jin Kyu Gahm |
| author_sort | Won June Choi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e5788e405f5c4f61b3964d3ce75c50db |
| institution | OA Journals |
| issn | 1095-9572 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-e5788e405f5c4f61b3964d3ce75c50db2025-08-20T02:34:19ZengElsevierNeuroImage1095-95722024-12-0130412094010.1016/j.neuroimage.2024.120940Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learningWon June Choi0Jin HwangBo1Quan Anh Duong2Jae-Hyeok Lee3Jin Kyu Gahm4Department of Information Convergence Engineering, Pusan National University, Busan 46241, South KoreaDepartment of Neurology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan 50612, South KoreaDepartment of Information Convergence Engineering, Pusan National University, Busan 46241, South KoreaDepartment of Neurology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50612, South Korea; Medical Research Institute, Pusan National University School of Medicine, Yangsan 50612, South Korea; Corresponding author.School of Computer Science and Engineering, Pusan National University, Busan 46241, South Korea; Center for Artificial Intelligence Research, Pusan National University, Busan 46241, South Korea; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S1053811924004373Atypical parkinsonian syndrome (APS)Contrastive learningFew-shot learningHyperbolic spaceSusceptibility-weighted imaging (SWI) |
| spellingShingle | Won June Choi Jin HwangBo Quan Anh Duong Jae-Hyeok Lee Jin Kyu Gahm Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning NeuroImage Atypical parkinsonian syndrome (APS) Contrastive learning Few-shot learning Hyperbolic space Susceptibility-weighted imaging (SWI) |
| title | Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning |
| title_full | Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning |
| title_fullStr | Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning |
| title_full_unstemmed | Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning |
| title_short | Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning |
| title_sort | differentiating atypical parkinsonian syndromes with hyperbolic few shot contrastive learning |
| topic | Atypical parkinsonian syndrome (APS) Contrastive learning Few-shot learning Hyperbolic space Susceptibility-weighted imaging (SWI) |
| url | http://www.sciencedirect.com/science/article/pii/S1053811924004373 |
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