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Kewen Xia

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5 papers

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JBHI Journal 2025 Journal Article

Fine-Grained Hierarchical Progressive Modal-Aware Network for Brain Tumor Segmentation

Chenggang Lu
Jianwei Zhang
Dan Zhang
Lei Mou
Jinli Yuan
Kewen Xia
Zhitao Guo
Jiong Zhang

Brain tumors are highly lethal and debilitating pathological changes that require timely diagnosis and treatment. Magnetic resonance imaging (MRI), a non-invasive diagnostic tool, provides complementary multi-modal information crucial for accurate tumor detection and delineation. However, existing methods struggle to effectively fuse multi-modal information from MRI sequences and often fail to perform modality-specific feature extraction, which hinders accurate tumor segmentation. Furthermore, the inherent challenges posed by the blurred boundaries and complex morphological characteristics of tumor structures present additional substantial obstacles to achieving precise segmentation. To address these issues, we propose FiHam, a fine-grained hierarchical progressive modal-aware network that introduces a novel multi-modal fusion strategy and an advanced feature extraction mechanism. Specifically, FiHam employs a progressive fusion strategy that extracts modality-specific features at lower levels and integrates multi-modal features at higher levels to effectively leverage complementary information from tumor images. Additionally, we design a gated cross-attention modal-fusion module that adaptively selects and integrates dual-modal features using cross-attention mechanisms to enhance modality fusion. To further refine segmentation accuracy, we incorporate a tiny U-Net into the encoder to capture boundary features and complex tumor morphology. Extensive experiments on three large-scale, multi-modal brain tumor datasets demonstrate that FiHam achieves state-of-the-art performance, delivering significant improvements in segmentation accuracy and generalizability across diverse MRI modalities.

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EAAI Journal 2024 Journal Article

LAACNet: Lightweight adaptive activation convolution network-based defect detection on polished metal surfaces

Zhongliang Lv
Zhenyu Lu
Kewen Xia
Hailun Zuo
Xiangyu Jia
Honglian Li
Youwei Xu

After the metal workpiece has been polished, there may still be small defects on the surface, which will adversely affect the quality of the product and the service life and availability of the metal in severe cases. To solve this problem, the industrial sector has been seeking more lightweight and efficient solutions. Therefore, this thesis proposes a Lightweight Adaptive Activation Convolution Network (LAACNet). Firstly, this thesis proposes a lightweight convolution module that adopts features concatenation to realize intra-channel and inter-channel information transfer and fusion. Secondly, this thesis proposes an adaptive activation convolution module with the enhanced nonlinear expression of the module, which makes the deep neural network expression more powerful. This thesis proposes a spatial channel coordinate attention module to capture the long-range dependencies between image pixels better. Finally, this thesis introduces a loss function that can optimize performance in target classification and localization tasks. Experiments were conducted on the self-made datasets Metal Surface Defect-Detection (MSD-DET) and GC10-Detection (GC10-DET), achieving Mean Average Precision 50 (mAP50) of 86. 3% and 66. 8%, respectively. The detection performance of this model is superior to other methods. In the ablation experiment, this thesis verified the effectiveness of each module. This thesis validated the Northeastern University-Detection (NEU-DET) dataset, achieving a mAP50 of 76. 0%. The results show that LAACNet exhibits excellent robustness and generalization performance in surface defect detection. In addition, the method significantly reduces the number of model parameters, providing an effective choice for lightweight and efficient solutions.

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EAAI Journal 2023 Journal Article

Oil Logging Reservoir Recognition Based on TCN and SA-BiLSTM Deep Learning Method

Wenbiao Yang
Kewen Xia
Shurui Fan

The use of Deep Learning methods to mine useful and critical information from massive and complex logging datasets is of great importance for oil logging reservoir recognition. TCN-SA-BiLSTM was proposed due to the lack of previous studies to mine the internal correlation of the features of the logging dataset. TCN-SA-BiLSTM is a deep learning model that hybridizes Temporal Convolutional Network (TCN), Self-Attention mechanism (SA), and Bidirectional Long Short Term Memory network (BiLSTM). First, for the pre-processed feature data, TCN is used for feature extraction with parallel convolution operation. Then, by exploiting the ability of SA to extract the internal autocorrelation of time series features, this can better capture the dependence of feature data over long distances. Finally, the contextual linkage of the features is further obtained using BiLSTM. The experimental results show that TCN-SA-BiLSTM exhibits excellent performance in comparison with seven competing models on all performance evaluation metrics. It overcomes the deficiencies in capability exhibited by traditional logging interpretation techniques to improve the efficiency and success rate of oil and gas exploration.

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EAAI Journal 2023 Journal Article

Self-Attention Causal Dilated Convolutional Neural Network for Multivariate Time Series Classification and Its Application

Wenbiao Yang
Kewen Xia
Zhaocheng Wang
Shurui Fan
Ling Li

Time Series Classification (TSC) in data mining is gradually developing as an important research direction. Many researchers have developed an extensive interest in Multivariate Time Series Classification (MTSC). The Self-Attention Causal Dilated Convolutional Neural Network (SACDCNN) is proposed to address the limitations of existing models that perform poorly on classification tasks. It designs the residual and dense blocks based on Causal Dilated Convolution based on the traditional residual and dense networks that still have superior performance after deepening the network hierarchy and the dependence of time series on long-range information. A Self-Attention mechanism (SA) is also incorporated to extract the internal autocorrelation of time series features. Comparison experiments on 20 benchmark University of California, Riverside (UCR) and University of California, Irvine (UCI) datasets with eight high-performance classification models show that the method can improve the classification accuracy of time series datasets. Finally, it was applied to petroleum logging reservoir recognition, and a comparison experiment was conducted on two wells. The results show that SACDCNN is effective and significantly superior. It overcomes the shortcomings of traditional logging interpretation techniques and improves the efficiency and success rate of oil and gas exploration.

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EAAI Journal 2022 Journal Article

A Multi-Strategy Whale Optimization Algorithm and Its Application

Wenbiao Yang
Kewen Xia
Shurui Fan
Li Wang
Tiejun Li
Jiangnan Zhang
Yu Feng

Whale Optimization Algorithm (WOA) is a key tool for solving complex engineering optimization problems, aiming at adjusting important parameters to satisfy constraints and optimal objectives. WOA has a simple structure, few parameters, high search capability, and easy implementation. However, it suffers from the same problems as other metaheuristic algorithms of being prone to local optima and slow convergence, for which the Multi-Strategy Whale Optimization Algorithm (MSWOA) is proposed. Four strategies are introduced in MSWOA. Firstly, a highly randomized chaotic logistic map is used to generate a high-quality initial population. Secondly, exploitation and exploration are enhanced by setting adaptive weights and dynamic convergence factors. Further, a Lévy flight mechanism is introduced to maintain the population diversity in each iteration. Finally, the Evolutionary Population Dynamics (EPD) mechanism is introduced to improve the efficiency of search agents in finding the optimum. Another problem lies in the Semi-Supervised Extreme Learning Machine (SSELM) based on manifold regularization is an effective classification and regression model, but the random generation of input weights and hidden layer thresholds and the grid selection of hyperparameters lead to unsatisfactory classification performance. To this end, we developed the MSWOA-SSELM model, optimally selected the parameters of SSLEM using MSWOA, and applied it to logging layer recognition, which effectively improved the accuracy of logging interpretation. By comparing the experiments with 14 swarm intelligence algorithms on 18 benchmark test functions, the CEC2017 benchmark suite, and an engineering application problem, the experimental results show that MSWOA is significantly superior and effective in solving global optimization problems. Finally, the proposed MSWOA-SSELM is applied in three wells and outperforms other classification models in terms of Accuracy (ACC), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). It obtained the best results with 96. 2567% ACC, MAE of 0. 0749, and RMSE of 0. 3870.

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