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

arXiv:2311.14902v2 Announce Type: replace
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 extraction of more robust …

abstract arxiv classification clinical cs.cv data deep learning disease features fusion graph image images manifold medical multimodal parkinson parkinson's parkinson's disease prediction prior research type view work