The invention discloses a multimodal brain network feature fusion method based on multi-task learning, and the multimodal brain network feature fusion method based on the multi-task learning includes the steps of preprocessing the obtained functional magnetic resonance imaging (fMRI) images and ...
Overall we provide a fully automated multimodal fusion network that can be trained in an end-to-end manner without requiring preprocessing or labor-intensive labeling of the data. In this work, we developed and compared diverse multimodal fusion model architectures for risk stratification of PE. ...
A new method for multimodal sensor fusion is introduced. The technique relies on a two-stage process. In the first stage, a multimodal generative model is constructed from unlabelled training data. In the second stage, the generative model serves as a reconstruction prior and the search manifold...
Here, we present a Heterogeneous Graph neural network for Multimodal neuroimaging fusion learning (HGM). Traditional GNN-based models usually assume the brain network is a homogeneous graph with single type of nodes and edges. However, vast literatures have shown the heterogeneity of the human brain...
Then, a Siamese convolutional neural network (sCNN) is utilized for weighted fusion of important features from the two multimodal images. Simultaneously, a fractional order total generalized variation (FOTGV) is implemented for noise removal with improved degree of freedom. The image processing results...
Multimodal medical image fusion based on IHS and PCA The fusion of multimodal brain imaging for a given clinical application is a very important performance. Generally, the PET image indicates the brain fun... C He,H Wang - 中科院合肥物质科学研究院 被引量: 59发表: 2010年 Artificial ...
All participants received 75 μg of LSD, administered intravenously via a 10ml solution infused over a two minutes’ period, followed by an infusion of saline. The administration was followed by an acclimatization period of approximately 60 min, in which (for at least some of the time) ...
The ability of EEG signals to identify changes in human brain states has made researchers analyze the emotion with this signal. However, only limited resea... S Qiu,N Sekhar,P Singhal 被引量: 0发表: 2023年 Deep learning based multimodal emotion recognition using model-level fusion of audio-...
In recent years, with the advancement of convolutional neural network (CNN) and deep learning technology, these innovations have demonstrated remarkable performance in computer vision and image processing. They have also assumed a pivotal role in medical image fusion. Non-end-to-end deep learning-bas...
Beyond the characteristics of a brain lesion, such as its etiology, size or location, lesion network mapping (LNM) has shown that similar symptoms after a lesion reflects similar dis-connectivity patterns, thereby linking symptoms to brain networks. Here