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Yonghui Xu

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9 papers

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JBHI Journal 2026 Journal Article

Generation and Selection: A Self-Iterative Two-Stage Data Augmentation Method for Automated ECG Classification

Chaoying Jiang
Yujing Xin
Ning Liu
Yonghui Xu
Lei Liu
Lizhen Cui
Jianyong Wang

Automated electrocardiogram (ECG) classification tasks play a crucial role in clinical but face challenges due to the scarcity of accessible and well-labeled data. ECG data augmentation is an effective way to address these challenges, either by altering the characteristics of real ECG data or using statistical and generative models to generate labeled data. However, the generated data often suffer from noise in both the data and label, which can reduce the performance of classification models. To address this, we propose a novel self-iterative two-stage data augmentation method for automated ECG classification, called SiTs-ECG. In the generation stage, an unconditional diffusion model, guided by a Transformer encoder, is trained to capture the complex characteristics of long-term ECG signals, generating high-quality ECG-like samples. In the selection stage, the generated samples are assigned pseudo-labels by a well-trained base classification model, and those generated samples for which the model can confidently predict the pseudo-labels are selected. We then integrate these stages into a self-iterative training process to continually improve the performance of base classification model. Extensive experiments on three real-world datasets demonstrate the effectiveness of our method. Notably, on the Apnea-ECG dataset, using ECG-Transformer as the downstream classification model, Precision, Recall, F1, and Accuracy are improved by 7. 9, 9. 1, 9. 2, and 7. 3 percentage points, respectively. Furthermore, our method is versatile and compatible with various generative and downstream classification models, showing promising applications in automated ECG classification in the clinical field.

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JBHI Journal 2025 Journal Article

Finer-Grained Dynamic Functional Graph Structure Learning for EEG Sequence Modeling

Xiaofang Sun
Hangwei Qian
Yonghui Xu
Yongqing Zheng
Lizhen Cui

Electroencephalography (EEG) serves as a critical neuroimaging observation instrument to understand brain dynamics, yet accurately modeling the dynamic functional connectivity inherent in EEG signals remains an open challenge. The existing statistically based coarse-grained associations based on fixed physical distances or static graph structures cannot reflect the spatio-temporal specificity of dynamic functional interactions between brain regions. The secondary computation based on threshold or attention mechanisms to eliminate redundant connections cannot dynamically obtain global structural changes at a fine-grained level, ignoring the rapid dynamic reorganization of brain region functions and lacking a fast response to input correlations. More recently, state space models have been shown to excel at processing long data sequences. However, directly applying such models to EEG data is far from satisfactory due to the lack of functional connectivity modeling between brain regions. In this paper, we propose the Dynamic Functional Graph Structure Learning framework (DFGSL) to capture the dynamic functional connectivity with state space models at a finer-grained level. The proposed DFGSL first constructs dynamic similarity probability maps to reveal information exchange between brain regions. Then, we simulate the entire dynamic evolution of dynamic functional connectivity at a finer-grained level through a selective state space model. By calculating the dynamic similarity probability between selected states, we obtain a compact state representation to describe the context of the dynamic evolution of brain state and reveal potential neural mechanisms. Empirical experiments on three benchmark datasets with different populations, electrode numbers, and brain states show that the proposed DFGSL consistently outperforms state-of-the-art methods, demonstrating strong functional modeling capabilities.

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JBHI Journal 2025 Journal Article

Multi-Modal Disease Prediction With Hierarchical Self-Supervised Learning

Zhe Qu
Taihua Chen
Xin Zhou
Fanglin Zhu
Wei Guo
Yonghui Xu
Yixin Zhang
Lizhen Cui

The proliferation of healthcare data sources, including diverse imaging modalities and biochemical measurements, has created unprecedented opportunities for comprehensive disease prediction. Multi-modal clinical data, encompassing medical imaging reports, biochemical assays, and longitudinal clinical records, provides a rich foundation for developing sophisticated diagnostic models. Graph Neural Networks (GNNs) have emerged as a leading methodological framework, distinguished by their capacity to model complex inter-patient relationships and capture community structures within patient data. Despite their promise, current GNN-based approaches exhibit limitations in handling noisy, low-quality data and often impose overly restrictive graph smoothness constraints. These limitations can obscure patient-specific variations and compromise model robustness. To overcome these challenges, we propose HierSSL ( Hier archical S elf- S upervised L earning), a novel multi-modal disease prediction framework that enhances representational learning through dual-scale self-supervision mechanisms operating at both local and global levels. HierSSL's architecture specifically addresses two critical aspects: 1) the capture of local inter-modality dependencies and global community patterns, and 2) the optimization of multi-modal feature integration through an innovative combination of feature consistency constraints and graph contrastive learning. Empirical evaluation across two distinct disease prediction datasets demonstrates that HierSSL achieves statistically significant performance improvements compared to state-of-the-art methods, highlighting its efficacy in robust multi-modal data integration for disease prediction tasks.

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TAAS Journal 2024 Journal Article

A Hierarchical Model for Complex Adaptive System: From Adaptive Agent to AI Society

Deyu Zhou
Xiao Xue
Xudong Lu
Yuwei Guo
Peilin Ji
Hongtao Lv
Wei He
Yonghui Xu

As complex adaptive system involves human and social factors (e.g., changing demands, competition and collaboration among agents), accurately modeling the complex features of adaptive agents and AI society is crucial for the effective analysis and governance of complex adaptive systems. However, existing modeling methods struggle to accurately represent these complex features, there is a gap between existing technologies and complex features modeling. In this context, this paper proposes a hierarchical model based on the computational experiments method, which consists of four layers (i.e., L1, L2, L3 and L4) modeling the autonomous, evolutionary, interactive, and emergent features respectively from adaptive agent to AI society. Additionally, taking intelligent transportation system as an example, a computational experiments system is constructed to demonstrate the effectiveness of the proposed model. This model builds a bridge between complex feature modeling and various technologies, thereby offering theoretical support for further research in complex adaptive systems.

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JBHI Journal 2024 Journal Article

Medicine Package Recommendation via Dual-Level Interaction Aware Heterogeneous Graph

Fanglin Zhu
Xu Zhang
Batuo Zhang
Yonghui Xu
Lizhen Cui

Medicine package recommendation aims to assist doctors in clinical decision-making by recommending appropriate packages of medicines for patients. Current methods model this task as a multi-label classification or sequence generation problem, focusing on learning relationships between individual medicines and other medical entities. However, these approaches uniformly overlook the interactions between medicine packages and other medical entities, potentially resulting in a lack of completeness in recommended medicine packages. Furthermore, medicine commonsense knowledge considered by current methods is notably limited, making it challenging to delve into the decision-making processes of doctors. To solve these problems, we propose DIAGNN, a Dual-level Interaction Aware heterogeneous Graph Neural Network for medicine package recommendation. Specifically, DIAGNN explicitly models interactions of medical entities within electronic health records(EHRs) at two levels, individual medicine and medicine package, leveraging a heterogeneous graph. A dual-level interaction aware graph convolutional network is utilized to capture semantic information in the medical heterogeneous graph. Additionally, we incorporate medication indications into the medical heterogeneous graph as medicine commonsense knowledge. Extensive experimental results on real-world datasets validate the effectiveness of the proposed method.

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IJCAI Conference 2024 Conference Paper

Personalized Federated Learning for Cross-City Traffic Prediction

Yu Zhang
Hua Lu
Ning Liu
Yonghui Xu
Qingzhong Li
Lizhen Cui

Traffic prediction plays an important role in urban computing. However, many cities face data scarcity due to low levels of urban development. Although many approaches transfer knowledge from data-rich cities to data-scarce cities, the centralized training paradigm cannot uphold data privacy. For the sake of inter-city data privacy, Federated Learning has been used, which follows a decentralized training paradigm to enhance traffic knowledge of data-scarce cities. However, spatio-temporal data heterogeneity causes client drift, leading to unsatisfactory traffic prediction performance. In this work, we propose a novel personalized Federated learning method for Cross-city Traffic Prediction (pFedCTP). It learns traffic knowledge from multiple data-rich source cities and transfers the knowledge to a data-scarce target city while preserving inter-city data privacy. In the core of pFedCTP lies a Spatio-Temporal Neural Network (ST-Net) for clients to learn traffic representation. We decouple the ST-Net to learn space-independent traffic patterns to overcome cross-city spatial heterogeneity. Besides, pFedCTP adaptively interpolates the layer-wise global and local parameters to deal with temporal heterogeneity across cities. Extensive experiments on four real-world traffic datasets demonstrate significant advantages of pFedCTP over representative state-of-the-art methods.

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AAAI Conference 2023 Conference Paper

MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series

Qianwen Meng
Hangwei Qian
Yong Liu
Lizhen Cui
Yonghui Xu
Zhiqi Shen

Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recently shown its promising representation learning capability in the absence of expert annotations. However, existing contrastive approaches generally treat each instance independently, which leads to false negative pairs that share the same semantics. To tackle this problem, we propose MHCCL, a Masked Hierarchical Cluster-wise Contrastive Learning model, which exploits semantic information obtained from the hierarchical structure consisting of multiple latent partitions for multivariate time series. Motivated by the observation that fine-grained clustering preserves higher purity while coarse-grained one reflects higher-level semantics, we propose a novel downward masking strategy to filter out fake negatives and supplement positives by incorporating the multi-granularity information from the clustering hierarchy. In addition, a novel upward masking strategy is designed in MHCCL to remove outliers of clusters at each partition to refine prototypes, which helps speed up the hierarchical clustering process and improves the clustering quality. We conduct experimental evaluations on seven widely-used multivariate time series datasets. The results demonstrate the superiority of MHCCL over the state-of-the-art approaches for unsupervised time series representation learning.

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AAAI Conference 2023 Conference Paper

MMTN: Multi-Modal Memory Transformer Network for Image-Report Consistent Medical Report Generation

Yiming Cao
Lizhen Cui
Lei Zhang
Fuqiang Yu
Zhen Li
Yonghui Xu

Automatic medical report generation is an essential task in applying artificial intelligence to the medical domain, which can lighten the workloads of doctors and promote clinical automation. The state-of-the-art approaches employ Transformer-based encoder-decoder architectures to generate reports for medical images. However, they do not fully explore the relationships between multi-modal medical data, and generate inaccurate and inconsistent reports. To address these issues, this paper proposes a Multi-modal Memory Transformer Network (MMTN) to cope with multi-modal medical data for generating image-report consistent medical reports. On the one hand, MMTN reduces the occurrence of image-report inconsistencies by designing a unique encoder to associate and memorize the relationship between medical images and medical terminologies. On the other hand, MMTN utilizes the cross-modal complementarity of the medical vision and language for the word prediction, which further enhances the accuracy of generating medical reports. Extensive experiments on three real datasets show that MMTN achieves significant effectiveness over state-of-the-art approaches on both automatic metrics and human evaluation.

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IJCAI Conference 2022 Conference Paper

Enhancing Sequential Recommendation with Graph Contrastive Learning

Yixin Zhang
Yong Liu
Yonghui Xu
Hao Xiong
Chenyi Lei
Wei He
Lizhen Cui
Chunyan Miao

The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an individual interaction sequence and learn model parameters solely based on the item prediction loss. Thus, they usually fail to learn appropriate sequence representations. This paper proposes a novel recommendation framework, namely Graph Contrastive Learning for Sequential Recommendation (GCL4SR). Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data. Moreover, GCL4SR uses subgraphs of WITG to augment the representation of each interaction sequence. Two auxiliary learning objectives have also been proposed to maximize the consistency between augmented representations induced by the same interaction sequence on WITG, and minimize the difference between the representations augmented by the global context on WITG and the local representation of the original sequence. Extensive experiments on real-world datasets demonstrate that GCL4SR consistently outperforms state-of-the-art sequential recommendation methods.

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