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.
MRN: Harnessing 2D Vision Foundation Models for Diagnosing Parkinson's Disease with Limited 3D MR Data
Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China（北京生物医学工程创新中心，生物科学与医学工程学院，北京航空航天大学，北京，中国）；Beijing Celebrain Technology Co., Ltd.（北京Celebrain技术有限公司）
Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive Learning
Department of Systems Engineering, Stevens Institute of Technology, Hoboken, New Jersey, USA（系统工程系，史蒂文斯理工学院）；Department of Nuclear Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan（高雄长庚纪念医院核医学部）；Institute of Statistics, National University of Kaohsiung, Kaohsiung, Taiwan（国立高雄大学统计研究所）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China（深圳先进技术研究院，中国科学院）
Autism Spectrum Disorder Classification with Interpretability in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen Key Laboratory of Intelligent Bioinformatics（深圳市智能生物信息学重点实验室）；Center for High Performance Computing, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院高性能计算中心）
Dopamine Transporter SPECT Image Classification for Neurodegenerative Parkinsonism via Diffusion Maps and Machine Learning Classifiers
Academia Sinica（中央研究院）；National Cheng-Kung University（国立成功大学）；National Sun Yat-sen University（国立中山大学）；Kaohsiung Chang Gung Memorial Hospital（高雄长庚纪念医院）；Chang Gung University College of Medicine（长庚大学医学院）
S2RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images
School of Computer China University of Geosciences Wuhan, China；School of Geography；Information Engineering China University of Geosciences Wuhan, China；School of Management Wuhan University of Science
Self-Supervised Cross-Encoder for Neurodegenerative Disease Diagnosis
School of Mathematics and Statistics, Glasgow, UK（数学与统计学学院，英国格拉斯哥）；Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK（计算机与信息科学系，斯特拉斯克莱德大学，英国格拉斯哥）
Unsupervised Contrastive Learning for Efficient and Robust Spectral Shape Matching
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Research Center for Data Hub and Security, Zhejiang Lab（浙江实验室数据枢纽与安全研究中心）
A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction
Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Indore, Indore 453552, India（计算机科学与工程系，印度理工学院（IIT）印多尔，印多尔453552，印度）
Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images
School of Software Engineering, Southeast University（东南大学软件学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及交叉应用关键实验室（东南大学））；Department of Computing and Information Sciences, Saint Francis University（圣弗朗西斯大学计算机与信息科学系）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
Neural-MCRL: Neural Multimodal Contrastive Representation Learning for EEG-based Visual Decoding
Shanghai Maritime University（上海海事大学）；Affiliated Lianyungang Hospital of Xuzhou Medical University（徐州医科大学附属连云港医院）；The Hong Kong Polytechnic University（香港理工大学）
Cross-domain EEG-based Emotion Recognition with Contrastive Learning
School of Software Engineering, Xi'an Jiaotong University, China（西安交通大学软件工程学院）；School of Computer Science and Technology, Xi'an Jiaotong University, China（西安交通大学计算机科学与技术学院）
MaCo-GAN: Manifold-Contrastive Adversarial Learning for Single Image Super-Resolution
Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea（电气工程与计算机科学系，全州科学技术院，全州，韩国）；Department of AI Convergence, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea（人工智能融合系，全州科学技术院，全州，韩国）
Exploring the Potential of Contrastive Language-Image Pre-training for Multi-Source Remote Sensing Data
School of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Space-based Computing System Research Center, Zhejiang Lab（之江实验室天基计算系统研究中心）
Large-Scale Hyperspectral Image Clustering Using Contrastive Learning
China University of Geosciences（中国地质大学）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿罗森多夫亥姆霍兹中心）；Helmholtz Institute Freiberg for Resource Technology（弗赖贝格资源技术亥姆霍兹研究所）；Institute of Advanced Research in Artificial Intelligence（人工智能高级研究院）；National University of Defense Technology（国防科技大学）
