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Dayu Hu

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7 papers

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JBHI Journal 2026 Journal Article

DGAN-MPCC: A Novel Dual-GAN Enhanced Multi-Positive Contrastive Clustering Method for Omics Data

Jingxuan Wang
Jing Yang
Muhammad Attique Khan
Por Lip Yee
Jamel Baili
Dayu Hu

AI-driven clustering methods have significantly enhanced the capacity of researchers to explore the heterogeneity inherent in single-cell omics data, which is a crucial aspect of understanding complex biological systems in healthcare. Despite advancements, most existing methods still face challenges, such as (1) inherent sparsity and noise in cell data, which frequently lead to overfitting in networks. To address this, some researchers have proposed using Generative Adversarial Networks (GANs), however, the conventional single GAN architecture primarily focuses on simple data enhancement and lacks the capacity to infer complex biological data, thus leading to suboptimal clustering performance. (2) Contrastive learning has been proposed to obtain high-quality clustering structures; however, existing methods predominantly rely on a single positive pair, which prevents them from modeling and learning continuous transitions in cell states and thus hinders the establishment of feature representations sensitive to cell types. To address these issues, we propose a novel Dual-GAN Enhanced Multi-Positive Contrastive Clustering Method, DGAN-MPCC, tailored for low-quality single-cell data. Specifically, we propose using two independent GANs to simultaneously enhance the quality of both the input and bottleneck layers, thereby refining the generated cell embedding. Additionally, we have developed a multi-positive contrastive clustering framework that adaptively defines a multi-positive set from clustering structures, enabling each sample to establish positive relationships with all samples within the same cluster, thereby diversifying supervisory signals within the same class. Extensive experiments on several real-world single-cell datasets demonstrate that DGAN-MPCC surpasses current methods across multiple scenarios, providing a more robust and efficient tool for AI-driven decision-making in healthcare.

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AAAI Conference 2026 Conference Paper

Graph Masked Autoencoder for Multi-view Remote Sensing Data Clustering

Renxiang Guan
Junhong Li
Siwei Wang
Tianrui Liu
Dayu Hu
Miaomiao Li
Xinwang Liu

Multi-view graph clustering (MVGC) for remote sensing data has gained increasing attention due to its ability to integrate complementary information across modalities while capturing spatial dependencies in heterogeneous data. Although current methods based on graph contrastive learning achieve strong performance, they often misidentify intra-cluster samples as negatives, leading to class conflicts and reduced clustering accuracy. Graph masked autoencoders have recently shown promising potential in learning robust representations through masked reconstruction, but their application to remote sensing data remains underexplored. This challenge is especially notable in the multi-view remote sensing setting, where high heterogeneity and complex spatial structures increase the difficulty of effective representation learning. To address these issues, we propose Clustering-Guided graph Mask AutoEncoder (CG-MAE), the first framework to extend graph masked autoencoders to multi-view remote sensing clustering. We introduce a clustering-guided masking strategy that selectively masks nodes near cluster centers and intra-cluster edges, which are crucial for capturing key structural information. By reconstructing these masked components, the model is encouraged to focus on learning features that are highly relevant to clustering. To further improve training stability and efficiency, we design an easy-to-hard node masking strategy that enables the model to gradually learn from increasingly challenging patterns. Additionally, we propose a dual self-adaptive learning mechanism that encourages the model to align more closely with the underlying semantic distributions. Extensive experiments on four widely used multi-view remote sensing datasets demonstrate that CG-MAE consistently outperforms state-of-the-art methods in both clustering accuracy and representation quality.

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AAAI Conference 2026 Conference Paper

Learn from Global Correlations: Enhancing Evolutionary Algorithm via Spectral GNN

Kaichen Ouyang
Zong Ke
Shengwei Fu
Lingjie Liu
Puning Zhao
Dayu Hu

Evolutionary algorithms (EAs) are optimization algorithms that simulate natural selection and genetic mechanisms. Despite advancements, existing EAs have two main issues: (1) they rarely update next-generation individuals based on global correlations, thus limiting comprehensive learning; (2) it is challenging to balance exploration and exploitation, excessive exploitation leads to premature convergence to local optima, while excessive exploration results in an excessively slow search. Existing EAs heavily rely on manual parameter settings, inappropriate parameters might disrupt the exploration-exploitation balance, further impairing model performance. To address these challenges, we propose a novel evolutionary algorithm framework called Graph Neural Evolution (GNE). Unlike traditional EAs, GNE represents the population as a graph, where nodes correspond to individuals, and edges capture their relationships, thus effectively leveraging global information. Meanwhile, GNE utilizes spectral graph neural networks (GNNs) to decompose evolutionary signals into their frequency components and designs a filtering function to fuse these components. High-frequency components capture diverse global information, while low-frequency components capture more consistent information. This explicit frequency filtering strategy directly controls global-scale features through frequency components, overcoming the limitations of manual parameter settings and making the exploration-exploitation control more interpretable and effective. Extensive evaluations on nine benchmark functions (e.g., Sphere, Rastrigin, and Rosenbrock) demonstrate that GNE consistently outperforms both classical algorithms (GA, DE, CMA-ES) and advanced algorithms (SDAES, RL-SHADE) under various conditions, including original, noise-corrupted, and optimal solution deviation scenarios. GNE achieves solution quality several orders of magnitude better than other algorithms (e.g., 3.07e-20 mean on Sphere vs. 1.51e-07).

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EAAI Journal 2025 Journal Article

Artificial neural networks for finger vein recognition: A survey

Yimin Yin
Renye Zhang
Pengfei Liu
Wanxia Deng
Dayu Hu
Siliang He
Chen Li
Jinghua Zhang

Finger vein recognition is an emerging biometric recognition technology. Different from the other biometric features on the body surface, the venous vascular tissue of the fingers is buried deep inside the skin. Due to this advantage, finger vein recognition is highly stable and private. Finger veins are virtually impossible to steal and difficult to interfere with by external conditions. Unlike the finger vein recognition methods based on traditional machine learning, the artificial neural network technique, especially deep learning, does not rely on feature engineering and has superior performance. To summarize the development of finger vein recognition based on artificial neural networks, this paper collects 174 related papers. First, we introduce the background of finger vein recognition and the motivation for this survey. Then, the development history of artificial neural networks and the representative networks on finger vein recognition tasks are introduced. The public datasets widely used in finger vein recognition are then described. After that, we summarize the related finger vein recognition tasks based on classical neural networks and deep neural networks, respectively. Finally, the challenges and potential development directions in finger vein recognition are discussed. This paper provides a comprehensive and novel summary of the application of artificial neural networks in the finger vein recognition field.

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JBHI Journal 2025 Journal Article

Diffusion Model with Relation-Aware Attention and Edge-Aware Constraint for Multi-Modal Brain Tumor Segmentation

Xu Xu
Jing Yang
Dayu Hu
Muhammad Attique Khan
Lip Yee Por
Congsheng Li

Multi-modal brain tumors segmentation is a critical step for diagnosing and monitoring brain-related disease. Many studies have developed models for this task, but two challenges remain, i. e. , weak feature aggregation and poorly segmented edges. To address these issues, we develop an improved Diffusion model with relation-aware attention and edge-aware constraint, namely Diff-RE, for multi-modal brain tumor segmentation. Specifically, the volume data and noisy segmentation label map are paralleled fed into encoder module to extract high-level features. During training, weights are shared to ensure consistency. Then, extracted features are channel-wise concatenated and passed through the relation-aware attention module, which enhances appearance features using global structural relationships. Finally, the decoder module processes the attention-enhanced features to generate segmentation results. To improve boundary accuracy, an edge-aware constraint module is introduced during training. Our framework is trained and evaluated using three benchmark datasets, i. e. , BraTS 2018, 2019, and 2020. Experimental results demonstrate that Diff-RE is effective and highlight its superiority over peer methods.

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AAAI Conference 2025 Conference Paper

Structure-Adaptive Multi-View Graph Clustering for Remote Sensing Data

Renxiang Guan
Wenxuan Tu
Siwei Wang
Jiyuan Liu
Dayu Hu
Chang Tang
Yu Feng
Junhong Li

Multi-view clustering (MVC) for remote sensing data is a critical and challenging task in Earth observation. Although recent advances in graph neural network (GNN)-based MVC have shown remarkable success, the most prevalent approaches have two major limitations: 1) heavily relying on a predefined yet fixed graph, which limits the performance of clustering because the large number of indistinguishable background samples contained in remote sensing data would introduce noise information and increase structure heterogeneity; 2) ignoring the effect of confusing samples on cluster structure compactness, which leads to fluffy cluster structure and decrease feature discriminability. To address these issues, we propose a Structure-Adaptive Multi-View Graph Clustering method named SAMVGC on remote sensing data which boosts the structure homogeneity and cluster compactness by adaptively learning the graph and cluster structures, respectively. Concretely, we use the geometric structure within the feature embedding space to refine adjacency matrices. The adjacency matrices are dynamically fused with the previous ones to improve the homogeneity and stability of structure information. Additionally, the samples are separated into two categories, including the central (intra-cluster center samples) and the confusing (inter-cluster boundary samples). On the basis, we deploy the contrastive learning paradigm on the central samples within views and the consistent learning paradigm on the confusing samples between views, improving the cluster compactness and consistency. Finally, we conduct extensive experiments on four benchmarks and achieve promising results, well demonstrating the effectiveness and superiority of the proposed method.

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JBHI Journal 2024 Journal Article

Dual-Channel Prototype Network for Few-Shot Pathology Image Classification

Hao Quan
Xinjia Li
Dayu Hu
Tianhang Nan
Xiaoyu Cui

In the field of pathology, the scarcity of certain diseases and the difficulty of annotating images hinder the development of large, high-quality datasets, which in turn affects the advancement of deep learning-assisted diagnostics. Few-shot learning has demonstrated unique advantages in modeling tasks with limited data, yet explorations of this method in the field of pathology remain in the early stages. To address this issue, we present a dual-channel prototype network (DCPN), a novel few-shot learning approach for efficiently classifying pathology images with limited data. The DCPN leverages self-supervised learning to extend the pyramid vision transformer (PVT) to few-shot classification tasks and combines it with a convolutional neural network to construct a dual-channel network for extracting multi-scale, high-precision pathological features, thereby substantially enhancing the generalizability of prototype representations. Additionally, we design a soft voting classifier based on multi-scale features to further augment the discriminative power of the model in complex pathology image classification tasks. We constructed three few-shot classification tasks with varying degrees of domain shift using three publicly available pathological datasets—CRCTP, NCTCRC, and LC25000—to emulate real-world clinical scenarios. The results demonstrated that the DCPN outperformed the prototypical network across all metrics, achieving the highest accuracies in same-domain tasks—70. 86% for 1-shot, 82. 57% for 5-shot, and 85. 2% for 10-shot setups—corresponding to improvements of 5. 51%, 5. 72%, and 6. 81%, respectively, over the prototypical network. Notably, in the same-domain 10-shot setting, the accuracy of the DCPN (85. 2%) surpassed that of the PVT-based supervised learning model (85. 15%), confirming its potential to diagnose rare diseases within few-shot learning frameworks.

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