Search Results for author: Yanjun Lyu

Found 7 papers, 0 papers with code

Core-Periphery Principle Guided Redesign of Self-Attention in Transformers

no code implementations27 Mar 2023 Xiaowei Yu, Lu Zhang, Haixing Dai, Yanjun Lyu, Lin Zhao, Zihao Wu, David Liu, Tianming Liu, Dajiang Zhu

Designing more efficient, reliable, and explainable neural network architectures is critical to studies that are based on artificial intelligence (AI) techniques.

Gyri vs. Sulci: Disentangling Brain Core-Periphery Functional Networks via Twin-Transformer

no code implementations31 Jan 2023 Xiaowei Yu, Lu Zhang, Haixing Dai, Lin Zhao, Yanjun Lyu, Zihao Wu, Tianming Liu, Dajiang Zhu

To solve this fundamental problem, we design a novel Twin-Transformer framework to unveil the unique functional roles of gyri and sulci as well as their relationship in the whole brain function.

Anatomy

Representing Brain Anatomical Regularity and Variability by Few-Shot Embedding

no code implementations26 May 2022 Lu Zhang, Xiaowei Yu, Yanjun Lyu, Zhengwang Wu, Haixing Dai, Lin Zhao, Li Wang, Gang Li, Tianming Liu, Dajiang Zhu

Our experimental results show that: 1) the learned embedding vectors can quantitatively encode the commonality and individuality of cortical folding patterns; 2) with the embeddings we can robustly infer the complicated many-to-many anatomical correspondences among different brains and 3) our model can be successfully transferred to new populations with very limited training samples.

Few-Shot Learning

Disentangling Spatial-Temporal Functional Brain Networks via Twin-Transformers

no code implementations20 Apr 2022 Xiaowei Yu, Lu Zhang, Lin Zhao, Yanjun Lyu, Tianming Liu, Dajiang Zhu

In this work, we propose a novel Twin-Transformers framework to simultaneously infer common and individual functional networks in both spatial and temporal space, in a self-supervised manner.

Representative Functional Connectivity Learning for Multiple Clinical groups in Alzheimer's Disease

no code implementations14 Jun 2021 Lu Zhang, Xiaowei Yu, Yanjun Lyu, Li Wang, Dajiang Zhu

By mapping the learned clinical group related feature vectors to the original FC space, representative FCs were constructed for each group.

Multi-class Classification

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