ArticleProceedings. IEEE International Symposium on Biomedical Imaging2024
PARKINSON'S DISEASE CLASSIFICATION USING CONTRASTIVE GRAPH CROSS-VIEW LEARNING WITH MULTIMODAL FUSION OF SPECT IMAGES AND CLINICAL FEATURES.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- 3D Spatiotemporal cardiac reconstruction for predicting MACE in acute myocardial infarction.NPJ digital medicine · 2026Article
- Multimodal intelligent prediction model for in vitro fertilization.NPJ digital medicine · 2026Article
- AI in SPECT Imaging: Opportunities and Challenges.Seminars in nuclear medicine · 2025Review
- Self-Supervised Learning to Unveil Brain Dysfunctional Signatures in Brain Disorders: Methods and Applications.Health data science · 2025Review
- Article
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Authors and funding
3 authors.
Funding
Abstract
Parkinson's Disease (PD) affects millions globally, impacting movement. Prior research utilized deep learning for PD prediction, primarily focusing on medical images, neglecting the data's underlying manifold structure. This work proposes a multimodal approach encompassing both image and non-image features, leveraging contrastive cross-view graph fusion for PD classification. We introduce a novel multimodal co-attention module, integrating embeddings from separate graph views derived from low-dimensional representations of images and clinical features. This enables more robust and structured feature extraction for improved multi-view data analysis. Additionally, a simplified contrastive loss-based fusion method is devised to enhance cross-view fusion learning. Our graph-view multimodal approach achieves an accuracy of 91% and an area under the receiver operating characteristic curve (AUC) of 92.8% in five-fold cross-validation. It also demonstrates superior predictive capabilities on non-image data compared to solely machine learning-based methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.