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学者姓名：孙光民
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摘要 ：
In the emerging field of image segmentation, Fully Convolutional Networks (FCNs) have recently become prominent. However, their effectiveness is intimately linked with the correct selection and fine-tuning of hyperparameters, which can often be a cumbersome manual task. The main aim of this study is to propose a more efficient, less labour-intensive approach to hyperparameter optimization in FCNs for segmenting fundus images. To this end, our research introduces a hyperparameter-optimized Fully Convolutional Encoder-Decoder Network (FCEDN). The optimization is handled by a novel Genetic Grey Wolf Optimization (G-GWO) algorithm. This algorithm employs the Genetic Algorithm (GA) to generate a diverse set of initial positions. It leverages Grey Wolf Optimization (GWO) to fine-tune these positions within the discrete search space. Testing on the Indian Diabetic Retinopathy Image Dataset (IDRiD), Diabetic Retinopathy, Hypertension, Age-related macular degeneration and Glacuoma ImageS (DR-HAGIS), and Ocular Disease Intelligent Recognition (ODIR) datasets showed that the G-GWO method outperformed four other variants of GWO, GA, and PSO-based hyperparameter optimization techniques. The proposed model achieved impressive segmentation results, with accuracy rates of 98.5% for IDRiD, 98.7% for DR-HAGIS, and 98.4%, 98.8%, and 98.5% for different sub-datasets within ODIR. These results suggest that the proposed hyperparameter-optimized FCEDN model, driven by the G-GWO algorithm, is more efficient than recent deep-learning models for image segmentation tasks. It thereby presents the potential for increased automation and accuracy in the segmentation of fundus images, mitigating the need for extensive manual hyperparameter adjustments.
关键词 ：
FCN FCN grey wolf optimization grey wolf optimization artificial intelligence artificial intelligence Diabetic eye disease Diabetic eye disease image segmentation image segmentation deep learning deep learning CNN CNN
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| GB/T 7714 | Khan, Abdul Qadir , Sun, Guangmin , Li, Yu et al. Optimizing Fully Convolutional Encoder-Decoder Network for Segmentation of Diabetic Eye Disease [J]. | CMC-COMPUTERS MATERIALS & CONTINUA , 2023 , 77 (2) : 2481-2504 . |
| MLA | Khan, Abdul Qadir et al. "Optimizing Fully Convolutional Encoder-Decoder Network for Segmentation of Diabetic Eye Disease" . | CMC-COMPUTERS MATERIALS & CONTINUA 77 . 2 (2023) : 2481-2504 . |
| APA | Khan, Abdul Qadir , Sun, Guangmin , Li, Yu , Bilal, Anas , Manan, Malik Abdul . Optimizing Fully Convolutional Encoder-Decoder Network for Segmentation of Diabetic Eye Disease . | CMC-COMPUTERS MATERIALS & CONTINUA , 2023 , 77 (2) , 2481-2504 . |
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摘要 ：
Background: With the rapid advancement of medical imaging technology, the demand for accurate segmentation of medical images is increasing. However, most existing methods are unable to capture locality and long-range dependency information in integrated ways for medical images.Method: In this paper, we propose an elegant segmentation framework for medical images named TC-Net, which can utilize both the locality-aware and long-range dependencies in the medical images. As for the locality-aware perspective, we employ a CNN-based encoder and decoder structure. The CNN branch uses the locality of convolution operations to dig out local information in medical images. As for the long-range dependencies, we construct a Transformer branch to focus on the global context. Additionally, we proposed a locality-aware and long-range dependency concatenation strategy (LLCS) to aggregate the feature maps obtained from the two subbranches. Finally, we present a dynamic cyclical focal loss (DCFL) to address the class imbalance problem in multi-lesion segmentation.Results: Comprehensive experiments were conducted on lesion segmentation tasks using two fundus image da-tabases and a skin image database. The TC-Net achieves scores of 0.6985 and 0.5171 in the metric of mean pixel accuracy on the IDRiD and DDR databases, respectively. Moreover, on the skin image database, the TC-Net reached mean pixel accuracy of 0.8886. The experiment results demonstrate that the proposed method ach-ieves better performance than other deep learning segmentation schemes. Furthermore, the proposed DCFL achieves higher performance than other loss functions in multi-lesion segmentation.Significance: The proposed TC-Net is a promising new framework for multi-lesion medical image segmentation and many other challenging image segmentation tasks. (c) 2001 Elsevier Science. All rights reserved.
关键词 ：
Class-imbalance Class-imbalance Convolutional neural network Convolutional neural network Vision transformer Vision transformer Medical image segmentation Medical image segmentation
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| GB/T 7714 | Zhang, Zhongxiang , Sun, Guangmin , Zheng, Kun et al. TC-Net: A joint learning framework based on CNN and vision transformer for multi-lesion medical images segmentation [J]. | COMPUTERS IN BIOLOGY AND MEDICINE , 2023 , 161 . |
| MLA | Zhang, Zhongxiang et al. "TC-Net: A joint learning framework based on CNN and vision transformer for multi-lesion medical images segmentation" . | COMPUTERS IN BIOLOGY AND MEDICINE 161 (2023) . |
| APA | Zhang, Zhongxiang , Sun, Guangmin , Zheng, Kun , Yang, Jin-Kui , Zhu, Xiao-rong , Li, Yu . TC-Net: A joint learning framework based on CNN and vision transformer for multi-lesion medical images segmentation . | COMPUTERS IN BIOLOGY AND MEDICINE , 2023 , 161 . |
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摘要 ：
深部探测工程光学钻孔成像系统涉及地质勘测及光学钻孔成像领域。整体可分为地上系统和地下系统两部分。地上系统是可视化上位机界面；地下系统包括图像采集模块、钻孔内壁图像变换模块、图像融合拼接模块、图像压缩模块，图像采集模块用于俯视拍摄地下钻孔图像；图像变换模块用于将钻孔内壁俯视图片中的中心黑洞区域去除并将剩余的有效区域进行透射变换展开并矫正；图像融合拼接模块用于将矫正后的内壁正视图像拼接为一幅完整的钻孔内壁平面图像；图像压缩模块是将成品图进行压缩处理，方便上传至上位机。地上系统与地下系统通过tcp/ip通信模块进行信息交互。本发明能有效地在井下钻孔图像上传时节约带宽，增加了钻孔内壁图像的有效信息量。
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| GB/T 7714 | 孙光民 , 刘凡 . 深部探测工程光学钻孔成像系统 : CN202210334680.5[P]. | 2022-03-30 . |
| MLA | 孙光民 et al. "深部探测工程光学钻孔成像系统" : CN202210334680.5. | 2022-03-30 . |
| APA | 孙光民 , 刘凡 . 深部探测工程光学钻孔成像系统 : CN202210334680.5. | 2022-03-30 . |
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摘要 ：
本公开实施例涉及一种基于自监督学习的OCTA图像分类结构训练方法，包括：基于无标签信息的B‑scan OCTA图像序列对模型进行自监督学习，直至重建的B‑scan OCTA图像序列与给定的B‑scan OCTA图像序列之间的重构误差、重建OCTA特征图像与融合OCTA特征图像的重构误差满足预设条件；将给定的带标签信息的en‑face OCTA图像对自监督学习后的模型中的二维随机掩码特征编码模块、全连接层、softmax层进行微调式训练，获得用于对任一用户的OCTA图像进行分类的二维随机掩码特征编码模块，该二维随机掩码特征编码模块作为OCTA图像分类结构。本发明对基于人体视网膜en‑face OCTA图像的疾病分析提供依据，使分类结果更准确，分类准确率更高。
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| GB/T 7714 | 孙光民 , 汤长新 , 李煜 et al. 基于自监督学习的OCTA图像分类结构训练方法 : CN202210887658.3[P]. | 2022-07-26 . |
| MLA | 孙光民 et al. "基于自监督学习的OCTA图像分类结构训练方法" : CN202210887658.3. | 2022-07-26 . |
| APA | 孙光民 , 汤长新 , 李煜 , 张忠祥 . 基于自监督学习的OCTA图像分类结构训练方法 : CN202210887658.3. | 2022-07-26 . |
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摘要 ：
一种基于基尔霍夫圆和透射变换算法的钻孔内壁图像展开与矫正方法，属于井下管道勘测技术领域。针对现有井下电视系统中上传图像时对于通信带宽的浪费，本发明提出了一种对井下钻孔中采集的图像进行有效信息区域的提取并还原钻孔内壁的方法。首先，通过基尔霍夫圆算法检测出钻孔内部俯视图像中无效信息黑洞区域，确定界限后将其去除；其次，运用极坐标的数学方法将有效区域图像进行圆环界限划定并展开为梯形；最后运用透射变换算法将展开的梯形图像进行四角拉伸还原钻孔内壁正视图。相对于现有钻孔光学成像系统，本发明能够有效地在井下钻孔图像上传时节约带宽，提高了采集效率，具有实际应用价值。
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| GB/T 7714 | 孙光民 , 刘凡 . 一种基于霍夫圆检测与透射变换算法的钻孔内壁图像展开与矫正方法 : CN202210368222.3[P]. | 2022-03-30 . |
| MLA | 孙光民 et al. "一种基于霍夫圆检测与透射变换算法的钻孔内壁图像展开与矫正方法" : CN202210368222.3. | 2022-03-30 . |
| APA | 孙光民 , 刘凡 . 一种基于霍夫圆检测与透射变换算法的钻孔内壁图像展开与矫正方法 : CN202210368222.3. | 2022-03-30 . |
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摘要 ：
Purpose The purpose of this paper is to overcome the application limitations of other multi-variable regression based on polynomials due to the huge computation room and time cost. Design/methodology/approach In this paper, based on the idea of feature selection and cascaded regression, two strategies including Laguerre polynomials and manifolds optimization are proposed to enhance the accuracy of multi-variable regression. Laguerre polynomials were combined with the genetic algorithm to enhance the capacity of polynomials approximation and the manifolds optimization method was introduced to solve the co-related optimization problem. Findings Two multi-variable Laguerre polynomials regression methods are designed. Firstly, Laguerre polynomials are combined with feature selection method. Secondly, manifolds component analysis is adopted in cascaded Laguerre polynomials regression method. Two methods are brought to enhance the accuracy of multi-variable regression method. Research limitations/implications With the increasing number of variables in regression problem, the stable accuracy performance might not be kept by using manifold-based optimization method. Moreover, the methods mentioned in this paper are not suitable for the classification problem. Originality/value Experiments are conducted on three types of datasets to evaluate the performance of the proposed regression methods. The best accuracy was achieved by the combination of cascade, manifold optimization and Chebyshev polynomials, which implies that the manifolds optimization has stronger contribution than the genetic algorithm and Laguerre polynomials.
关键词 ：
Cascaded regression Cascaded regression Genetic algorithm Genetic algorithm Feature selection Feature selection Polynomials regression Polynomials regression Manifolds optimization Manifolds optimization Laguerre polynomials Laguerre polynomials
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| GB/T 7714 | Li, Zibo , Yan, Zhengxiang , Li, Shicheng et al. Comparative study for multi-variable regression methods based on Laguerre polynomial and manifolds optimization [J]. | ENGINEERING COMPUTATIONS , 2022 , 39 (8) : 3058-3082 . |
| MLA | Li, Zibo et al. "Comparative study for multi-variable regression methods based on Laguerre polynomial and manifolds optimization" . | ENGINEERING COMPUTATIONS 39 . 8 (2022) : 3058-3082 . |
| APA | Li, Zibo , Yan, Zhengxiang , Li, Shicheng , Sun, Guangmin , Wang, Xin , Zhao, Dequn et al. Comparative study for multi-variable regression methods based on Laguerre polynomial and manifolds optimization . | ENGINEERING COMPUTATIONS , 2022 , 39 (8) , 3058-3082 . |
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摘要 ：
Objective. As an extension of optical coherence tomography (OCT), optical coherence tomographic angiography (OCTA) provides information on the blood flow status at the microlevel and is sensitive to changes in the fundus vessels. However, due to the distinct imaging mechanism of OCTA, existing models, which are primarily used for analyzing fundus images, do not work well on OCTA images. Effectively extracting and analyzing the information in OCTA images remains challenging. To this end, a deep learning framework that fuses multilevel information in OCTA images is proposed in this study. The effectiveness of the proposed model was demonstrated in the task of diabetic retinopathy (DR) classification. Method. First, a U-Net-based segmentation model was proposed to label the boundaries of large retinal vessels and the foveal avascular zone (FAZ) in OCTA images. Then, we designed an isolated concatenated block (ICB) structure to extract and fuse information from the original OCTA images and segmentation results at different fusion levels. Results. The experiments were conducted on 301 OCTA images. Of these images, 244 were labeled by ophthalmologists as normal images, and 57 were labeled as DR images. An accuracy of 93.1% and a mean intersection over union (mIOU) of 77.1% were achieved using the proposed large vessel and FAZ segmentation model. In the ablation experiment with 6-fold validation, the proposed deep learning framework that combines the proposed isolated and concatenated convolution process significantly improved the DR diagnosis accuracy. Moreover, inputting the merged images of the original OCTA images and segmentation results further improved the model performance. Finally, a DR diagnosis accuracy of 88.1% (95%CI +/- 3:6%) and an area under the curve (AUC) of 0.92 were achieved using our proposed classification model, which significantly outperforms the state-of-the-art classification models. As a comparison, an accuracy of 83.7 (95%CI +/- 1:5%) and AUC of 0.76 were obtained using EfficientNet. Significance. The visualization results show that the FAZ and the vascular region close to the FAZ provide more information for the model than the farther surrounding area. Furthermore, this study demonstrates that a clinically sophisticated designed deep learning model is not only able to effectively assist in the diagnosis but also help to locate new indicators for certain illnesses.
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| GB/T 7714 | Li, Qiaoyu , Zhu, Xiao-rong , Sun, Guangmin et al. Diagnosing Diabetic Retinopathy in OCTA Images Based on Multilevel Information Fusion Using a Deep Learning Framework [J]. | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE , 2022 , 2022 . |
| MLA | Li, Qiaoyu et al. "Diagnosing Diabetic Retinopathy in OCTA Images Based on Multilevel Information Fusion Using a Deep Learning Framework" . | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022 (2022) . |
| APA | Li, Qiaoyu , Zhu, Xiao-rong , Sun, Guangmin , Zhang, Lin , Zhu, Meilong , Tian, Tian et al. Diagnosing Diabetic Retinopathy in OCTA Images Based on Multilevel Information Fusion Using a Deep Learning Framework . | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE , 2022 , 2022 . |
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摘要 ：
Objective: Diabetic retinopathy is the leading cause of vision loss in working-age adults. Early screening and diagnosis can help to facilitate subsequent treatment and prevent vision loss. Deep learning has been applied in various fields of medical identification. However, current deep learning-based lesion segmentation techniques rely on a large amount of pixel-level labeled ground truth data, which limits their performance and application. In this work, we present a weakly supervised deep learning framework for eye fundus lesion segmentation in patients with diabetic retinopathy. Methods: First, an efficient segmentation algorithm based on grayscale and morphological features is proposed for rapid coarse segmentation of lesions. Then, a deep learning model named Residual-Attention Unet (RAUNet) is proposed for eye fundus lesion segmentation. Finally, a data sample of fundus images with labeled lesions and unlabeled images with coarse segmentation results is jointly used to train RAUNet to broaden the diversity of lesion samples and increase the robustness of the segmentation model. Results: A dataset containing 582 fundus images with labels verified by doctors, including hemorrhage (HE), microaneurysm (MA), hard exudate (EX) and soft exudate (SE), and 903 images without labels was used to evaluate the model. In ablation test, the proposed RAUNet achieved the highest intersection over union (IOU) on the labeled dataset, and the proposed attention and residual modules both improved the IOU of the UNet benchmark. Using both the images labeled by doctors and the proposed coarse segmentation method, the weakly supervised framework based on RAUNet architecture significantly improved the mean segmentation accuracy by over 7% on the lesions. Significance: This study demonstrates that combining unlabeled medical images with coarse segmentation results can effectively improve the robustness of the lesion segmentation model and proposes a practical framework for improving the performance of medical image segmentation given limited labeled data samples.
关键词 ：
lesion segmentation lesion segmentation diabetic retinopathy diabetic retinopathy weak supervision weak supervision deep learning deep learning fundus image fundus image
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| GB/T 7714 | Li, Yu , Zhu, Meilong , Sun, Guangmin et al. Weakly supervised training for eye fundus lesion segmentation in patients with diabetic retinopathy [J]. | MATHEMATICAL BIOSCIENCES AND ENGINEERING , 2022 , 19 (5) : 5293-5311 . |
| MLA | Li, Yu et al. "Weakly supervised training for eye fundus lesion segmentation in patients with diabetic retinopathy" . | MATHEMATICAL BIOSCIENCES AND ENGINEERING 19 . 5 (2022) : 5293-5311 . |
| APA | Li, Yu , Zhu, Meilong , Sun, Guangmin , Chen, Jiayang , Zhu, Xiaorong , Yang, Jinkui . Weakly supervised training for eye fundus lesion segmentation in patients with diabetic retinopathy . | MATHEMATICAL BIOSCIENCES AND ENGINEERING , 2022 , 19 (5) , 5293-5311 . |
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摘要 ：
In medical diagnostics, the invention of the computer-aided identification method has played a significant role in making essential decisions for human diseases. Lung cancer requires a greater focus among various diagnostic processes because both men and women are affected, contributing to high mortality rates. In addition, lung cancer is one of the leading causes of death worldwide. It can be treated if diagnosed at an early stage. Detecting and classifying lung lesions is challenging for radiologists. Radiologists typically use computer-aided diagnostic systems to screen for lung cancer. In recent years, computer specialists have proposed many techniques for diagnosing lung cancer. Conventional lung cancer prediction methods have failed to maintain the precision needed because the low-quality picture affects the segmentation process. Here, we propose a well-performing method to detect and classify lung cancer. We applied the Grey Wolf Optimization algorithm with a weighted filter to reduce noise in images, followed by segmentation using watershed transformation and dilation operations. In the end, we classified lung cancer among three classes using our method that showed high performance compared to previous studies: 98.33% accuracy, 100% sensitivity, and 93.33% specificity.
关键词 ：
Grey Wolf Optimization (GWO) Grey Wolf Optimization (GWO) Classification Classification Computer-Aided Diagnostic System (CAD) System Computer-Aided Diagnostic System (CAD) System Convolutional Neural Network (CNN) Convolutional Neural Network (CNN) Computed Tomography (CT) Computed Tomography (CT)
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| GB/T 7714 | Bilal, Anas , Sun, Guangmin , Li, Yu et al. Lung nodules detection using grey wolf optimization by weighted filters and classification using CNN [J]. | JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS , 2022 , 45 (2) : 175-186 . |
| MLA | Bilal, Anas et al. "Lung nodules detection using grey wolf optimization by weighted filters and classification using CNN" . | JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 45 . 2 (2022) : 175-186 . |
| APA | Bilal, Anas , Sun, Guangmin , Li, Yu , Mazhar, Sarah , Latif, Jahanzaib . Lung nodules detection using grey wolf optimization by weighted filters and classification using CNN . | JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS , 2022 , 45 (2) , 175-186 . |
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摘要 ：
基于SM9算法的移动互联网身份认证方案研究
关键词 ：
单服务器环境 单服务器环境 移动互联网 移动互联网 身份认证 身份认证 SM9算法 SM9算法
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| GB/T 7714 | 张昱 , 孙光民 , 李煜 et al. 基于SM9算法的移动互联网身份认证方案研究 [J]. | 张昱 , 2021 , (4) : 1-9 . |
| MLA | 张昱 et al. "基于SM9算法的移动互联网身份认证方案研究" . | 张昱 4 (2021) : 1-9 . |
| APA | 张昱 , 孙光民 , 李煜 , 信息网络安全 . 基于SM9算法的移动互联网身份认证方案研究 . | 张昱 , 2021 , (4) , 1-9 . |
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