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Zhongke Gao

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9 papers

1 author row

EAAI Journal 2026 Journal Article

A multi-modal multi-task learning network for intelligent parameter measurement in gas–liquid two-phase flow

Hanqing Chen
Zhiqiang Zhao
Bang Zhou
Ruiqi Wang
Mengyu Li
Wei Li
Jun Liu
Weidong Cao

Accurate identification of flow patterns and reliable measurement of phase fraction are fundamental for monitoring and control in gas–liquid two-phase flow systems. Conventional sensing and modeling approaches, however, are often constrained by limited spatial resolution and adaptability to dynamic operating conditions. A multi-modal multi-task learning network (MMLNet) is proposed, which integrates spatially distributed conductance time-series signals acquired from a custom-designed sensor with synchronized high-speed flow images. The network adopts a dual-branch architecture, where modality-specific backbones are constructed using multi-scale depthwise separable convolutions, followed by attention-driven cross-modal interaction and a per-token sample gate for adaptive fusion. Under a unified multi-task objective, MMLNet jointly optimizes flow pattern classification and gas volume fraction (GVF) regression, thereby exploiting the inherent correlation between the two tasks to improve accuracy and generalization. Experimental results show that MMLNet achieves 99. 88% accuracy in flow pattern classification, with a mean absolute error (MAE) of 0. 63%, and a mean absolute percentage error (MAPE) of 2. 23% for GVF prediction, outperforming state-of-the-art baselines. These results highlight the potential of MMLNet as a scalable soft-sensing solution for multiphase flow monitoring.

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JBHI Journal 2026 Journal Article

ML-TGNet: A Multi-Level Topology Guidance Network for Motor Imagery Decoding

Weidong Dang
Zichen Ren
Jialu Sun
Dongmei Lv
Zhangjin Xiong
Wei Guo
Zhongke Gao
Huijie Yu

Brain-computer interfaces (BCIs) based on motor imagery electroencephalogram (MI-EEG) signals have been extensively applied in various neural rehabilitation scenarios. However, existing methods primarily focus on designing complex architectures to extract spatio-temporal features from MI-EEG signals, often neglecting the brain dynamics information embedded within them. This oversight leads to the extraction of redundant information, ultimately reducing decoding performance. To address these challenges, we design a multi-level topology-guidance network (ML-TGNet) that leverages topological brain synchronization information to more effectively extract features related to MI tasks. ML-TGNet specifically comprises a multi-level topology guidance module, a feature pool module, and a multi-branch decoding module. To evaluate its performance, extensive experiments are conducted on three publicly available MI datasets: the BCI Competition IV-2a dataset, the High Gamma dataset, and the OpenBMI dataset. ML-TGNet achieves classification accuracies of 82. 33%, 96. 42%, and 85. 26% on these three datasets, respectively, outperforming current state-of-the-art models. These findings confirm the efficacy of using brain synchronization information to guide MI decoding, thereby opening a novel approach for EEG-based brain state decoding by integrating brain dynamics into deep learning.

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JBHI Journal 2025 Journal Article

Transformer-Based Weakly Supervised Learning for Whole Slide Lung Cancer Image Classification

Jianpeng An
Yong Wang
Qing Cai
Gang Zhao
Stephan Dooper
Geert Litjens
Zhongke Gao

Image analysis can play an important role in supporting histopathological diagnoses of lung cancer, with deep learning methods already achieving remarkable results. However, due to the large scale of whole-slide images (WSIs), creating manual pixel-wise annotations from expert pathologists is expensive and time-consuming. In addition, the heterogeneity of tumors and similarities in the morphological phenotype of tumor subtypes have caused inter-observer variability in annotations, which limits optimal performance. Effective use of weak labels could potentially alleviate these issues. In this paper, we propose a two-stage transformer-based weakly supervised learning framework called Simple Shuffle-Remix Vision Transformer (SSRViT). Firstly, we introduce a Shuffle-Remix Vision Transformer (SRViT) to retrieve discriminative local tokens and extract effective representative features. Then, the token features are selected and aggregated to generate sparse representations of WSIs, which are fed into a simple transformer-based classifier (SViT) for slide-level prediction. Experimental results demonstrate that the performance of our proposed SSRViT is significantly improved compared with other state-of-the-art methods in discriminating between adenocarcinoma, pulmonary sclerosing pneumocytoma and normal lung tissue (accuracy of 96. 9 ${\%}$ and AUC of 99. 6 ${\%}$ ).

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JBHI Journal 2025 Journal Article

Unsupervised Domain Adaptation With Synchronized Self-Training for Cross- Domain Motor Imagery Recognition

Peiyin Chen
Xiaofeng Liu
Chao Ma
He Wang
Xiong Yang
Celso Grebogi
Xiao Gu
Zhongke Gao

Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often rely on large amounts of annotated data collected through specific experimental setups, which fail to address the heterogeneity of data distributions across different domains. This limitation hinders BCI systems from effectively managing the complexity and variability of real-world data. To overcome these challenges, we propose Synchronized Self-Training Domain Adaptation (SSTDA) for cross-domain motor imagery classification. Specifically, SSTDA leverages labeled signals from a source domain and applies self-training to unlabeled signals from a target domain, enabling the simultaneous training of a more robust classifier. The raw EEG signals are mapped into a latent space by a feature extractor for discriminative representation learning. A domain-shared latent space is then learned by optimizing the feature extractor with both source and target samples, using an easy-tohard self-training process. We validate the method with extensive experiments on two public motor imagery datasets: Dataset IIa of BCI Competition IV and the High Gamma dataset. In the inter-subject task, our method achieves classification accuracies of 64. 43% and 80. 40%, respectively. It also outperforms existing methods in the inter-session task. Moreover, we develope a new six-class motor imagery dataset and achieve test accuracies of 77. 09% and 80. 18% across different datasets. All experimental results demonstrate that our SSTDA outperforms existing algorithms in inter-session, inter-subject, and inter-dataset validation protocols, highlighting its capability to learn discriminative, domain-invariant representations that enhance EEG decoding performance.

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EAAI Journal 2024 Journal Article

A novel multiphase flow water cut modeling framework based on flow behavior-heuristic deep learning

Weidong Dang
Dongmei Lv
Feng Jing
Ping Yu
Wei Guo
Zhongke Gao

Industry 4. 0 is of great significance for the development of oil industry. One of the pivotal steps towards achieving oil Industry 4. 0 is accurately mastering the oilfield production dynamics, especially the changes of process parameters. Among many process parameters, the accurate modeling of water cut is extremely critical and difficult. In this paper, under laboratory conditions, oil–water flows with various preset water cuts are simulated. A fluid sensor, equipped with eight concave-shaped conductive electrodes, is employed to capture multi-channel measurement data, continuously recording the oil–water flow process from multiple angles. Subsequently, a novel flow behavior-heuristic deep learning model, named FBHWC model, is developed to model the relationship between the measurement data and water cut, achieving water cut measurement. The FBHWC model is guided by the complex flow behaviors of oil–water flows and consists of two key modules. Particularly, the multi-level feature fusion module focuses on the high-order feature extraction and fusion of sensor measurement data, while the multi-scale measurement module uses fully convolutional design to achieve accurate measurement of water cut. Experimental results show that the FBHWC model has excellent performance in measuring water cut, with mean square error of 0. 016%. All these open up a new venue for exploring industrial multiphase flows through combining multi-electrode sensor and deep learning.

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JBHI Journal 2023 Journal Article

WS-MTST: Weakly Supervised Multi-Label Brain Tumor Segmentation With Transformers

Huazhen Chen
Jianpeng An
Bochang Jiang
Lili Xia
Yunhao Bai
Zhongke Gao

Brain tumor segmentation is a key step in brain cancer diagnosis. Segmentation of brain tumor sub-regions, including necrotic, enhancing, and edematous regions, can provide more detailed guidance for clinical diagnosis. Weakly supervised brain tumor segmentation methods have received much attention because they do not require time-consuming pixel-level annotations. However, existing weakly supervised methods focus on the segmentation of the entire tumor region while ignoring the challenging task of multi-label segmentation for the tumor sub-regions. In this article, we propose a weakly supervised approach to solve the multi-label brain tumor segmentation problem. To the best of our knowledge, it's the first end-to-end multi-label weakly supervised segmentation model applied to brain tumor segmentation. With well-designed loss functions and a contrastive learning pre-training process, our proposed Transformer-based segmentation method (WS-MTST) has the ability to perform segmentation of brain tumor sub-regions. We conduct comprehensive experiments and demonstrate that our method reaches the state-of-the-art on the popular brain tumor dataset BraTS (from 2018 to 2020).

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JBHI Journal 2021 Journal Article

Attention-Based Parallel Multiscale Convolutional Neural Network for Visual Evoked Potentials EEG Classification

Zhongke Gao
Xinlin Sun
Mingxu Liu
Weidong Dang
Chao Ma
Guanrong Chen

Electroencephalography (EEG) decoding is an important part of Visual Evoked Potentials-based Brain-Computer Interfaces (BCIs), which directly determines the performance of BCIs. However, long-time attention to repetitive visual stimuli could cause physical and psychological fatigue, resulting in weaker reliable response and stronger noise interference, which exacerbates the difficulty of Visual Evoked Potentials EEG decoding. In this state, subjects' attention could not be concentrated enough and the frequency response of their brains becomes less reliable. To solve these problems, we propose an attention-based parallel multiscale convolutional neural network (AMS-CNN). Specifically, the AMS-CNN first extract robust temporal representations via two parallel convolutional layers with small and large temporal filters respectively. Then, we employ two sequential convolution blocks for spatial fusion and temporal fusion to extract advanced feature representations. Further, we use attention mechanism to weight the features at different moments according to the output-related interest. Finally, we employ a full connected layer with softmax activation function for classification. Two fatigue datasets collected from our lab are implemented to validate the superior classification performance of the proposed method compared to the state-of-the-art methods. Analysis reveals the competitiveness of multiscale convolution and attention mechanism. These results suggest that the proposed framework is a promising solution to improving the decoding performance of Visual Evoked Potential BCIs.

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JBHI Journal 2021 Journal Article

COVID-19 Screening in Chest X-Ray Images Using Lung Region Priors

Jianpeng An
Qing Cai
Zhiyong Qu
Zhongke Gao

Early screening of COVID-19 is essential for pandemic control, and thus to relieve stress on the health care system. Lung segmentation from chest X-ray (CXR) is a promising method for early diagnoses of pulmonary diseases. Recently, deep learning has achieved great success in supervised lung segmentation. However, how to effectively utilize the lung region in screening COVID-19 still remains a challenge due to domain shift and lack of manual pixel-level annotations. We hereby propose a multi-appearance COVID-19 screening framework by using lung region priors derived from CXR images. Firstly, we propose a multi-scale adversarial domain adaptation network (MS-AdaNet) to boost the cross-domain lung segmentation task as the prior knowledge to the classification network. Then, we construct a multi-appearance network (MA-Net), which is composed of three sub-networks to realize multi-appearance feature extraction and fusion using lung region priors. At last, we can obtain prediction results from normal, viral pneumonia, and COVID-19 using the proposed MA-Net. We extend the proposed MS-AdaNet for lung segmentation task on three different public CXR datasets. The results suggest that the MS-AdaNet outperforms contrastive methods in cross-domain lung segmentation. Moreover, experiments reveal that the proposed MA-Net achieves accuracy of 98. 83 $\%$ and F1-score of 98. 71 $\%$ on COVID-19 screening. The results indicate that the proposed MA-Net can obtain significant performance on COVID-19 screening.

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JBHI Journal 2021 Journal Article

Rhythm-Dependent Multilayer Brain Network for the Detection of Driving Fatigue

Weidong Dang
Zhongke Gao
Dongmei Lv
Xinlin Sun
Chichao Cheng

Fatigue driving has attracted a great deal of attention for its huge influence on automobile accidents. Recognizing driving fatigue provides a primary but significant way for addressing this problem. In this paper, we first conduct the simulated driving experiments to acquire the EEG signals in alert and fatigue states. Then, for multi-channel EEG signals without pre-processing, a novel rhythm-dependent multilayer brain network (RDMB network) is developed and analyzed for driving fatigue detection. We find that there exists a significant difference between alert and fatigue states from the view of network science. Further, key sub-RDMB network based on closeness centrality are extracted. We calculate six network measures from the key sub-RDMB network and construct feature vectors to classify the alert and fatigue states. The results show that our method can respectively achieve the average accuracy of 95. 28% (with sample length of 5 s), 90. 25% (2 s), and 87. 69% (1 s), significantly higher than compared methods. All these validate the effectiveness of RDMB network for reliable driving fatigue detection via EEG.

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