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Xiaocheng Yang

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EAAI Journal 2026 Journal Article

Dynamic path smooth unfolding network and learnable random smoothing strategy for magnetic resonance imaging compressed sensing

Ziqi Yang
Mingfeng Jiang
Chenghu Geng
Zhifeng Chen
Mengyu Jia
Xiaocheng Yang
Sumei Huang
Feng Liu

Deep Unfolding Networks (DUNs) have become the mainstream approach for compressed sensing Magnetic Resonance Imaging (MRI) reconstruction from highly under-sampled k-space data. In this paper, a novel Dynamic Path Smooth Unfolding Network (DPSU-Net) is proposed for compressed sensing MRI reconstruction by dynamically selecting different paths for smooth unfolding. Furthermore, a learnable random smoothing strategy is used to enhance model robustness by introducing perturbations through a noise generator during training stage. Experimental results on the FastMRI T1-weighted and T2-weighted images show that DPSU-Net achieves superior reconstruction performance across different under-sampling rates, with Peak Signal-to-Noise Ratio (PSNR)/Structural Similarity Index Measure (SSIM) of 48. 70/0. 9889 on T1-weighted images and 45. 68/0. 9715 on T2-weighted images, surpassing existing state-of-the-art networks. Ablation studies further confirm the effectiveness and robustness of the dynamic path selection and learnable random smoothing strategies, demonstrating improvements in reconstruction quality.

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AAAI Conference 2023 Conference Paper

Simple and Efficient Heterogeneous Graph Neural Network

Xiaocheng Yang
Mingyu Yan
Shirui Pan
Xiaochun Ye
Dongrui Fan

Heterogeneous graph neural networks (HGNNs) have the powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) designed for homogeneous graphs, especially the attention mechanism and the multi-layer structure. These mechanisms bring excessive complexity, but seldom work studies whether they are really effective on heterogeneous graphs. In this paper, we conduct an in-depth and detailed study of these mechanisms and propose the Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN). To easily capture structural information, SeHGNN pre-computes the neighbor aggregation using a light-weight mean aggregator, which reduces complexity by removing overused neighbor attention and avoiding repeated neighbor aggregation in every training epoch. To better utilize semantic information, SeHGNN adopts the single-layer structure with long metapaths to extend the receptive field, as well as a transformer-based semantic fusion module to fuse features from different metapaths. As a result, SeHGNN exhibits the characteristics of a simple network structure, high prediction accuracy, and fast training speed. Extensive experiments on five real-world heterogeneous graphs demonstrate the superiority of SeHGNN over the state-of-the-arts on both accuracy and training speed.

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