765 citations · 2.1k across the 63 of their papers we have counts for
13 papers · 1 filter
Spatial-Temporal Convolutional Attention for Mapping Functional Brain Networks
Yiheng Liu, Enjie Ge, Ning Qiang +2
Using functional magnetic resonance imaging (fMRI) and deep learning to explore functional brain networks (FBNs) has attracted many researchers. However, most of these studies are…
Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
Hongmin Cai, Wenxiong Liao, Zhengliang Liu +12
Modern supervised learning neural network models require a large amount of manually labeled data, which makes the construction of domain-specific knowledge graphs time-consuming an…
BI AVAN: Brain inspired Adversarial Visual Attention Network
Heng Huang, Lin Zhao, Xintao Hu +4
Visual attention is a fundamental mechanism in the human brain, and it inspires the design of attention mechanisms in deep neural networks. However, most of the visual attention st…
Coupling Visual Semantics of Artificial Neural Networks and Human Brain Function via Synchronized Activations
Lin Zhao, Haixing Dai, Zihao Wu +8
Artificial neural networks (ANNs), originally inspired by biological neural networks (BNNs), have achieved remarkable successes in many tasks such as visual representation learning…
Rectify ViT Shortcut Learning by Visual Saliency
Chong Ma, Lin Zhao, Yuzhong Chen +7
Shortcut learning is common but harmful to deep learning models, leading to degenerated feature representations and consequently jeopardizing the model's generalizability and inter…
Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention
Yiheng Liu, Enjie Ge, Mengshen He +6
Using deep learning models to recognize functional brain networks (FBNs) in functional magnetic resonance imaging (fMRI) has been attracting increasing interest recently. However,…
Representing Brain Anatomical Regularity and Variability by Few-Shot Embedding
Lu Zhang, Xiaowei Yu, Yanjun Lyu +7
Effective representation of brain anatomical architecture is fundamental in understanding brain regularity and variability. Despite numerous efforts, it is still difficult to infer…
Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning
Chong Ma, Lin Zhao, Yuzhong Chen +15
Learning harmful shortcuts such as spurious correlations and biases prevents deep neural networks from learning the meaningful and useful representations, thus jeopardizing the gen…
Brain Cortical Functional Gradients Predict Cortical Folding Patterns via Attention Mesh Convolution
Li Yang, Zhibin He, Changhe Li +4
Since gyri and sulci, two basic anatomical building blocks of cortical folding patterns, were suggested to bear different functional roles, a precise mapping from brain function to…
A Unified and Biologically-Plausible Relational Graph Representation of Vision Transformers
Yuzhong Chen, Yu Du, Zhenxiang Xiao +8
Vision transformer (ViT) and its variants have achieved remarkable successes in various visual tasks. The key characteristic of these ViT models is to adopt different aggregation s…
Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning
Yuzhong Chen, Zhenxiang Xiao, Lin Zhao +10
Learning with little data is challenging but often inevitable in various application scenarios where the labeled data is limited and costly. Recently, few-shot learning (FSL) gaine…
Follow My Eye: Using Gaze to Supervise Computer-Aided Diagnosis
Sheng Wang, Xi Ouyang, Tianming Liu +2
When deep neural network (DNN) was first introduced to the medical image analysis community, researchers were impressed by its performance. However, it is evident now that a large…
Disentangling Spatial-Temporal Functional Brain Networks via Twin-Transformers
Xiaowei Yu, Lu Zhang, Lin Zhao +3
How to identify and characterize functional brain networks (BN) is fundamental to gain system-level insights into the mechanisms of brain organizational architecture. Current funct…