Parkinson's Disease Classification Using Contrastive Graph Cross-View Learning with Multimodal Fusion of SPECT Images and Clinical Features

Document detail

ID

oai:arXiv.org:2311.14902

Topic

Computer Science - Computer Vision...

Author

Ding, Jun-En Hsu, Chien-Chin Liu, Feng

Year

2023

listing date

8/28/2024

Keywords

pd features images

Metrics

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 0.91 and an area under the receiver operating characteristic curve (AUC) of 0.93 in five-fold cross-validation.

It also demonstrates superior predictive capabilities on non-image data compared to solely machine learning-based methods.

Ding, Jun-En,Hsu, Chien-Chin,Liu, Feng, 2023, Parkinson's Disease Classification Using Contrastive Graph Cross-View Learning with Multimodal Fusion of SPECT Images and Clinical Features