Yanwu Yang (杨延武)

yangyanwu1111@gmail.com

I am now a postdoc in University Tübingen Hospital and IMPRS-IS. My research interests span neuroimaging analysis (large-scale population studies, neurodevelopment, and aging), computational neuroscience (manifold learning and latent identity), and Connectome-Wide Association Studies (CWAS). I am particularly interested in developing deep learning methods for clinical applications and in using neuroscience to inform the analysis of foundation models, such as the emergence and reasoning mechanisms in LLMs. We are currently eager to collaborate on large-scale neuroimaging studies, including but not limited to lifespan research and foundation model investigations.

Studied topics:

  • Brain Connectome/Brain Network Analysis
  • Normative Modeling/Anomaly Detection
  • Universal Medical Image Segmentation

    Google Scholar Github

    • News


    Research

    Advancing Brain Imaging Analysis Step-by-step via Progressive Self-paced Learning
    Yanwu Yang, Hairui Chen, Jiesi Hu, Xutao Guo, Ting Ma*
    MICCAI, 2024.
    Paper /
    BrainMass: Advancing Brain Network Analysis for Diagnosis with Large-scale Self-Supervised Learning
    Yanwu Yang, Chenfei Ye, Guinan Su, Ziyao Zhang, Zhikai Chang, Hairui Chen, Piu Chan, Yue Yu* and Ting Ma*
    IEEE Transactions on Medical Imaging (TMI), 2024.
    Paper /
    Topology-Regularized Self-Knowledge Distillation for Transductive-Inductive Learning of Brain Disorder Diagnosis
    Yanwu Yang, Xutao Guo, Guoqing Cai, Chenfei Ye, and Ting Ma*
    IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 2022.
    Paper /
    CReg-KD: Model Refinement via Confidence Regularized Knowledge Distillation for Brain Imaging
    Yanwu Yang, Xutao Guo, Chenfei Ye, Yang Xiang*, and Ting Ma*
    Medical Image Analysis (MedIA), 2023.
    Paper / Code
    Mapping Multi-modal Brain Connectome for Brain Disorder Diagnosis via Cross-modal Mutual Learning
    Yanwu Yang, Chenfei Ye, Xutao Guo, Tao Wu, Yang Xiang*, and Ting Ma*
    IEEE Transactions on Medical Imaging (TMI), 2023.
    Paper / Code
    Incomplete Learning of Multimodal Connectome for Brain Disorder Diagnosis via Modal-mixup and Deep Supervision
    Yanwu Yang, Hairui Chen, Zhikai Chang, Yang Xiang, Chenfei Ye*, and Ting Ma*
    International Conference on Medical Imaging with Deep Learning, MIDL, 2023.
    Paper Code
    A Deep Connectome Learning Network Using Graph Convolution for Connectome-disease Association Study
    Yanwu Yang, Chenfei Ye, and Ting Ma*
    Neural Networks (NN), 2023.
    Paper
    Tensor-based Complex-valued Graph Neural Network for Dynamic Coupling Multimodal Brain Networks
    Yanwu Yang, Guoqing Cai, Chenfei Ye, Yang Xiang*, and Ting Ma*
    IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 2022.
    Paper
    Multi-modal Dynamic Graph Network: Coupling Structural and Functional Connectome for Disease Diagnosis and Classification
    Yanwu Yang, Xutao Guo, Zhikai Chang, Chenfei Ye, Yang Xiang*, and Ting Ma*
    IEEE International Conference on Bioinformatics and Biomedicine, (BIBM), 2021.
    Paper
    Regularizing Brain Age Prediction via Gated Knowledge Distillation
    Yanwu Yang, Xutao Guo, Chenfei Ye, Yang Xiang*, and Ting Ma*
    International Conference on Medical Imaging with Deep Learning, MIDL, 2022.
    Paper / Code
    Brain Age Vector: A Measure of Brain Aging with Enhanced Neurodegenerative Disorder Specificity
    Chen Ran, Yanwu Yang, Chenfei Ye, Haiyan Lv, and Ting Ma*
    Human Brain Mapping, 2022.
    Paper
    Alteration of Brain Structural Connectivity in Progression of Parkinson's Disease: A Connectome-wide Network Analysis
    Yanwu Yang, Chenfei Ye, Junyan Sun, Li Liang, Haiyan Lv, Linlin Gao, Jiliang Fang, Ting Ma*, and Tao Wu*
    NeuroImage: Clinical, 2021.
    Paper


    Competitions
    The Top in the Tecent AIMIS 2021 challenge
    on Brain Age Prediction

    The Top in MICCAI-QUBIQ-2021 challenge
    on Medical Image Segmentation Uncertainty Quantification




    Last Update: Nov 4, 2024

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