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Guangjie Han

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8 papers

1 author row

EAAI Journal 2026 Journal Article

A feature-aware attention selection network for anomaly detection on printed circuit boards

Zhenyu Yin
Li Qin
Feiqing Zhang
Youwei Yu
Xiaoqiang Shi
Guangjie Han
Yuanguo Bi

Self-supervised anomaly detection has emerged as a research hotspot in intelligent manufacturing and quality inspection, holding significant practical value in industrial applications. However, anomaly detection in real-world printed circuit board (PCB) production environments remains challenging. Existing methods often exhibit limited generalization when facing diverse anomaly types and environmental disturbances. In addition, high model complexity and insufficient capability for multi-scale fine-grained defect recognition constrain their practical deployment. To address these issues, this paper proposes a novel self-supervised anomaly detection framework (FSDNet). First, this paper proposes an Anomalous Sample Synthesizer Based on Diffusion Model (AnoDiff), which generates diverse and controllable anomalous samples to improve model generalization. Second, this paper designs an Anomaly Feature Perception Module (AFPM) that selects discriminative channels from pretrained features, thereby reducing model complexity while enhancing detection performance. Third, this paper proposes a Multi-scale Residual Reconstruction Network (MRRN) is developed to aggregate multi-scale features, improving sensitivity to fine-grained anomalies. Finally, this paper proposes two novel attention-based modules: a Top-k Sparse & Space Attention Module (TSSM) and a Gated Feature Enhancement Module (GFEM), both of which strengthen the discriminability and robustness of anomaly features. Experimental results demonstrate that the proposed method significantly outperforms state-of-the-art approaches on MVTec-AD, ViSA, and a self-constructed PCB dataset in terms of detection accuracy and robustness, validating its effectiveness and practical utility. The dataset and code are available at https://github.com/QinLi-STUDY/FSDNet/tree/master .

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JBHI Journal 2026 Journal Article

Synthesis Image Editing for Attribute Evolution in the Pseudo-Temporal Sequence of Pulmonary Nodule Growth

Hongbo Zhu
Bowen Liu
Xiaotong Wei
Guangjie Han
Wenbo Zhang
Yue Ma
Aso Darwesh

Medical Mixed Reality (MR) has made significant progress in virtual surgery simulation and tumor teaching. This paper proposes a framework for pulmonary nodule attribute editing based on image feature consistency, achieving spatial alignment of multi-stage case data. To address the limitations of traditional time-image reconstruction, we design an adversarial siamese model architecture capable of synthesizing missing nodule images, completing temporal data, and fine-grained modeling of nodule growth. To tackle challenges such as deformation, background inconsistency, and attribute uncertainty in generated samples, we introduce a Denoising Diffusion Implicit Model (DDIM) and construct an attribute vector space for pathological feature editing. Additionally, we propose a separable image reconstruction strategy to enhance local feature stability. Extensive validation on the lung-specific LIDC-IDRI dataset demonstrates superior performance with SSIM of 97. 5 ${\%}$ and LPIPS of 0. 036. To further verify generalization capability, cross-organ testing on the liver-focused LiTS dataset achieves competitive results with SSIM of 85. 0 ${\%}$ and LPIPS of 0. 128. These outcomes provide strong technical support for high-fidelity virtual surgery and intelligent tumor teaching platforms.

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EAAI Journal 2025 Journal Article

Feature Description Attention: Channel-independent local–global fusion for multi-scale feature representation

Yuanyang Zhu
Guangjie Han
Hongbo Zhu
Fan Zhang

In recent deep convolutional networks, attention mechanisms rely heavily on global information to generate attention weights, but few methods can effectively recalibrate global features based on local object characteristics. To address this gap, we introduce a masked-averaging strategy that adaptively selects regions of interest from the feature map, allowing local features to encode and reflect global information. By combining these local descriptors with global averages and maximums into a multi-scale Bag-of-Visual-Words (BoVW), our method jointly captures salient point, local region, and global context, resulting in richer and more discriminative feature representations. Additionally, we propose Feature Batch Normalization (FBN) to facilitate cross-channel interactions, further enhancing the performance of attention modules. In terms of engineering applications, to overcome the limitations of traditional channel-dependent attention under model compression and pruning, we design a channel-independent feature recalibration mechanism based on one-dimensional depthwise convolution, termed Feature Description Attention (FDA). It leverages the properties of BoVW and FBN to provide a flexible and lightweight attention module with minimal computational overhead and robust performance under quantization and pruning. Our method demonstrates consistent gains across three vision tasks: classification accuracy improves by 0. 72–1. 68%, object detection performance increases by 1. 8–3. 0% mean average precision, and segmentation accuracy rises by 1. 9%. These results across different benchmarks confirm the effectiveness and generalizability of our approach. FDA offers a scalable, hardware-friendly solution for modern vision applications, especially valuable in resource-constrained settings such as mobile deployment, autonomous systems, and medical image analysis.

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JBHI Journal 2024 Journal Article

EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT

Shiliang Shao
Guangjie Han
Ting Wang
Chuan Lin
Chunhe Song
Chen Yao

Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of Things technology will also enable the identification of mental workload remotely based on human physiological signals. In this article, a method based on the spatial and time–frequency domains of electroencephalography (EEG) signals is proposed to improve the classification accuracy of mental workload. Moreover, a hybrid deep learning model is presented. First, the spatial domain features of different brain regions are proposed. Simultaneously, EEG time–frequency domain information is obtained based on wavelet transform. The spatial and time–frequency domain features are input into two types of deep learning models for mental workload classification. To validate the performance of the proposed method, the Simultaneous Task EEG Workload public database is used. Compared with the existing methods, the proposed approach shows higher classification accuracy. It provides a novel means of assessing mental workload.

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JBHI Journal 2023 Journal Article

Knowledge Sharing for Pulmonary Nodule Detection in Medical Cyber-Physical Systems

Hongbo Zhu
Guangjie Han
Jianxia Hou
Xiangliang Liu
Yue Ma

With the rapid development of edge intelligence (EI) and machine learning (ML), the applications of Cyber-Physical Systems (CPS) have been discovered in all aspects of the life world. As one of its most essential branches, Medical CPS (MCPS) determines human health and medical treatment in the Internet of Everything (IOE) era. Knowledge sharing is the critical point of MCPS and has also been humanity's best dream through the ages. This paper explores a novel knowledge-sharing model in MCPS and takes a pulmonary nodule detection task as a significant case for building an Unet-based mask generator. A Classification-guided Module (CGM)-based discriminator with knowledge from EMRs is set against a generator to offer a promising result for each mask from the inexperienced participant of federated ML. After an iterative communication between the federated server and its clients for knowledge sharing, the segmented sub-image owns a coincident attribute distribution with that of the EMRs from the experts. Besides, the adversarial network augment the data to normalize the data distribution for all the clients as a remission for none independent identically distributed (non-IID) data problem. We implement a detection framework on the simulated EI environment following an existing adaptive synchronization strategy based on data sharing and median loss function. On 1304 scans of the merged dataset, our proposed framework can help boost the detection performance for most of the existing methods of pulmonary nodule detection.

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EAAI Journal 2023 Journal Article

MPDNet: An underwater image deblurring framework with stepwise feature refinement module

Guangjie Han
Min Wang
Hongbo Zhu
Chuan Lin

In this study, a general network model called multi-progressive image deblurring network is proposed to correct blurring artifacts and local imaging details in underwater images. As a solution to nonuniform image distortion, a deformable convolution module was designed to enrich the encoded information of the image representation. Using a stepwise feature refinement module, multi-progressive image deblurring network can reduce the loss of contextual information to produce a more realistic underwater image for subsequent applications. Constructing a loss function based on multi-scale content can help the model improve image perception quality. We conducted experimental evaluations on large-scale image deblurring benchmark datasets, such as GoPro and HIDE, achieving excellent results with 32. 84 dB and 31. 03 dB peak signal-to-noise ratio, respectively, using the proposed method. Subsequently, a detailed optimization comparison was conducted on the in-house underwater image deblurring dataset. Multi-progressive image deblurring network obtained higher-quality, clearer images. Compared with the current state-of-the-art image deblurring algorithms, the proposed model achieved significant results with a 6. 6% increase in deblur performance in peak signal-to-noise ratio. Finally, we conducted ablation experiments to evaluate the effectiveness of all the modules in the proposed framework.

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JBHI Journal 2022 Journal Article

Obstructive Sleep Apnea Detection Scheme Based on Manually Generated Features and Parallel Heterogeneous Deep Learning Model Under IoMT

Shiliang Shao
Guangjie Han
Ting Wang
Chunhe Song
Chen Yao
Jianxia Hou

Obstructive sleep apnea (OSA) syndrome is a common sleep disorder and a key cause of cardiovascular and cerebrovascular diseases that seriously affect the lives and health of people. The development of Internet of Medical Things (IoMT) has enabled the remote diagnosis of OSA. The physiological signals of human sleep are sent to the cloud or medical facilities through Internet of Things, after which diagnostic models are employed for OSA detection. In order to improve the detection accuracy of OSA, in this study, a novel OSA detection system based on manually generated features and utilizing a parallel heterogeneous deep learning model in the context of IoMT is proposed, and the accuracy of the proposed diagnostic model is investigated. The OSA recognition scheme used in our model is based on short-term heart rate variability (HRV) signals extracted from ECG signals. First, the HRV signals and the linear and nonlinear features of HRV are combined into a one-dimensional (1-D) sequence. Simultaneously, a two-dimensional (2-D) HRV time-frequency spectrum image is obtained. The 1-D data sequences and 2-D images are coded in different branches of the proposed deep learning network for OSA diagnosis. To validate the performance of the proposed scheme, the Physionet Apnea–ECG public database is used. The proposed scheme outperforms the existing methods in terms of accuracy and provides a novel direction for OSA recognition.

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JBHI Journal 2021 Journal Article

Two-Way MR-Forest Based Growing Path Classification for Malignancy Estimation of Pulmonary Nodules

Hongbo Zhu
Guangjie Han
Chuan Lin
Min Wang
Mohsen Guizani
Jianxia Hou
Wei Xing

This paper proposes a two-way multi-ringed forest (TMR-Forest) to estimating the malignancy of the pulmonary nodules for false positive reduction (FPR). Based on our previous work of deep decision framework, named MR-Forest, we generate a growing path mode on predefined pseudo-timeline of $L$ time slots to build pseudo-spatiotemporal features. It synchronously works with FPR based on MR-Forest to help predict the labels from a dynamic perspective. Concretely, Mask R-CNN is first used to recommend the bounding boxes of ROIs and classify their pathological features. Afterward, hierarchical attribute matching is introduced to obtain the input ROIs’ attribute layouts and select the candidates for their growing path generation. The selected ROIs can replace the fixed-sized ROIs’ fitting results at different time slots for data augmentation. A two-stage counterfactual path elimination is used to screen out the input paths of the cascade forest. Finally, a simple label selection strategy is executed to output the predicted label to point out the input nodule's malignancy. On 1034 scans of the merged dataset, the framework can report more accurate malignancy labels to achieve a better CPM score of 0. 912, which exceeds those of MR-Forest and 3DDCNNs about 2. 8% and 4. 7%, respectively.

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