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学者姓名：李建强
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摘要 ：
大气污染领域本体的半自动构建及语义推理
关键词 ：
语义推理 语义推理 注意力机制 注意力机制 大气污染 大气污染 自然语言处理 自然语言处理 实体关系抽取 实体关系抽取 本体 本体
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| GB/T 7714 | 刘博 , 张佳慧 , 李建强 et al. 大气污染领域本体的半自动构建及语义推理 [J]. | 刘博 , 2021 , 47 (3) : 246-259 . |
| MLA | 刘博 et al. "大气污染领域本体的半自动构建及语义推理" . | 刘博 47 . 3 (2021) : 246-259 . |
| APA | 刘博 , 张佳慧 , 李建强 , 李永 , 郎建垒 , 北京工业大学学报 . 大气污染领域本体的半自动构建及语义推理 . | 刘博 , 2021 , 47 (3) , 246-259 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明提供一种图像分割方法及系统，该方法包括：将待分割图像依次经过图像分割模型中的各下采样模块，获取最后一个下采样模块输出的特征图；将最后一个下采样模块输出的特征图依次经过图像分割模型中的各上采样模块，获取最后一个上采样模块输出的特征图；对最后一个上采样模块输出的特征图进行分割，获取最后一个上采样模块输出的特征图的分割结果；其中，任一上采样层的下一层的输入由该上采样层输出的特征图和将该上采样层所属的上采样模块对应的下采样模块输出的特征图输入金字塔池化层后输出的特征图融合获取。本发明实现融合后的特征图包含丰富的浅层特征和深层特征，可以减少特征信息的损失，有效提高图像分割的准确性。
引用：
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| GB/T 7714 | 李建强 , 刘青 . 图像分割方法及系统 : CN202110048037.1[P]. | 2021-01-14 . |
| MLA | 李建强 et al. "图像分割方法及系统" : CN202110048037.1. | 2021-01-14 . |
| APA | 李建强 , 刘青 . 图像分割方法及系统 : CN202110048037.1. | 2021-01-14 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The current mode of clinical aided diagnosis of Ocular Myasthenia Gravis (OMG) is time-consuming and laborious, and it lacks quantitative standards. An aided diagnostic system for OMG is proposed to solve this problem. The values calculated by the system include three clinical indicators: eyelid distance, sclera distance, and palpebra superior fatigability test time. For the first two indicators, the semantic segmentation method was used to extract the pathological features of the patient's eye image and a semantic segmentation model was constructed. The patient eye image was divided into three regions: iris, sclera, and background. The indicators were calculated based on the position of the pixels in the segmentation mask. For the last indicator, a calculation method based on the Eyelid Aspect Ratio (EAR) is proposed; this method can better reflect the change of eyelid distance overtime. The system was evaluated based on the collected patient data. The results show that the segmentation model achieves a mean Intersection-Over-Union (mIoU) value of 86.05%. The paired-sample T-test was used to compare the results obtained by the system and doctors, and the p values were all greater than 0.05. Thus, the system can reduce the cost of clinical diagnosis and has high application value.
关键词 ：
Convolution Convolution Facial features Facial features Faces Faces Image segmentation Image segmentation ocular myasthenia gravis ocular myasthenia gravis semantic segmentation semantic segmentation computer-aided system computer-aided system Standards Standards Eyelids Eyelids eyelid aspect ratio eyelid aspect ratio Feature extraction Feature extraction
引用：
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| GB/T 7714 | Liu, Guanjie , Wei, Yan , Xie, Yunshen et al. A Computer-Aided System for Ocular Myasthenia Gravis Diagnosis [J]. | TSINGHUA SCIENCE AND TECHNOLOGY , 2021 , 26 (5) : 749-758 . |
| MLA | Liu, Guanjie et al. "A Computer-Aided System for Ocular Myasthenia Gravis Diagnosis" . | TSINGHUA SCIENCE AND TECHNOLOGY 26 . 5 (2021) : 749-758 . |
| APA | Liu, Guanjie , Wei, Yan , Xie, Yunshen , Li, Jianqiang , Qiao, Liyan , Yang, Ji-Jiang . A Computer-Aided System for Ocular Myasthenia Gravis Diagnosis . | TSINGHUA SCIENCE AND TECHNOLOGY , 2021 , 26 (5) , 749-758 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
目的:利用人脸图像,构建基于深度学习的特纳综合征(Turner syndrome,TS)分类模型,旨在提高TS诊断准确率,降低诊断开销.方法:首先,将通道域注意力机制和空间域注意力机制以及残差结构相结合,提出一种具有混合域注意力模块的残差网络,然后使用深度迁移学习技术完成模型的初始化,最后使用TS人脸数据集对网络模型进行微调.结果:该模型对TS的分类准确率为0.9171.结论:所提出的TS分类模型优于现有TS识别方法,能更为有效地辅助TS的临床诊断.
关键词 ：
残差网络 残差网络 通道域注意力机制 通道域注意力机制 空间域注意力机制 空间域注意力机制 特纳综合征 特纳综合征
引用：
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| GB/T 7714 | 刘璐 , 李建强 , 陈适 . 基于混合域注意力机制和残差网络的特纳综合征分类研究 [J]. | 中国数字医学 , 2021 , 16 (2) : 16-20 . |
| MLA | 刘璐 et al. "基于混合域注意力机制和残差网络的特纳综合征分类研究" . | 中国数字医学 16 . 2 (2021) : 16-20 . |
| APA | 刘璐 , 李建强 , 陈适 . 基于混合域注意力机制和残差网络的特纳综合征分类研究 . | 中国数字医学 , 2021 , 16 (2) , 16-20 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明涉及一种眼底图像血管的识别方法、装置、电子设备及存储介质，该方法包括：获取待测眼底图像；基于检测算子，提取待测眼底图像的第一特征图像及第二特征图像；基于语义分割模型，提取待测眼底图像的空间形状特征图像；根据第一特征图像、第二特征图像及空间形状特征图像，重建待测眼底图像；将重建后的待测眼底图像输入血管分割模型，得到待测眼底图像的血管分割图像；其中，语义分割模型为根据眼底图像训练集训练得到的；血管分割模型为根据重建的眼底图像训练集训练得到的。本发明通过重建待测眼底图像，提升了图像清晰度，使图形特征更加明显，通过将重建后的眼底图像输入血管分割模型进行血管识别，得到了分割精度更高的血管分割图像。
引用：
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| GB/T 7714 | 吕思锐 , 李鹏智 , 杨鑫 et al. 一种眼底图像血管的识别方法、装置、电子设备及存储介质 : CN202110344357.1[P]. | 2021-03-30 . |
| MLA | 吕思锐 et al. "一种眼底图像血管的识别方法、装置、电子设备及存储介质" : CN202110344357.1. | 2021-03-30 . |
| APA | 吕思锐 , 李鹏智 , 杨鑫 , 李建强 . 一种眼底图像血管的识别方法、装置、电子设备及存储介质 : CN202110344357.1. | 2021-03-30 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
基于混合域注意力机制和残差网络的特纳综合征分类研究
关键词 ：
残差网络 残差网络 特纳综合征 特纳综合征 空间域注意力机制 空间域注意力机制 通道域注意力机制 通道域注意力机制
引用：
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| GB/T 7714 | 刘璐 , 李建强 , 陈适 et al. 基于混合域注意力机制和残差网络的特纳综合征分类研究 [J]. | 刘璐 , 2021 , 16 (2) : 16-20 . |
| MLA | 刘璐 et al. "基于混合域注意力机制和残差网络的特纳综合征分类研究" . | 刘璐 16 . 2 (2021) : 16-20 . |
| APA | 刘璐 , 李建强 , 陈适 , 中国数字医学 . 基于混合域注意力机制和残差网络的特纳综合征分类研究 . | 刘璐 , 2021 , 16 (2) , 16-20 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明提供一种药物推荐方法、装置、电子设备及存储介质。该方法包括：获取目标对象的相关信息；对所述相关信息进行隐私保护预处理；基于已进行隐私保护预处理的所述相关信息以及基于梯度提升决策树算法的模型来生成针对所述目标对象的药物推荐信息。本发明的药物推荐方法在准确、可靠的给患者推荐药物的同时，能够有效地保护患者的隐私；能够对不同类型的数据进行合适的隐私保护处理；推荐算法的鲁棒性较强；在不损失太多精度的情况下，更有效地保护患者的隐私。
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| GB/T 7714 | 李建强 , 李媛 , 王延安 . 药物推荐方法、装置、电子设备及存储介质 : CN202110022884.0[P]. | 2021-01-08 . |
| MLA | 李建强 et al. "药物推荐方法、装置、电子设备及存储介质" : CN202110022884.0. | 2021-01-08 . |
| APA | 李建强 , 李媛 , 王延安 . 药物推荐方法、装置、电子设备及存储介质 : CN202110022884.0. | 2021-01-08 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Both telomere length and alcohol consumption have an important impact on biological age and carcinogenesis. Researchers have conducted many efforts to study the relationship between alcohol consumption and telomere length yet reached no consensus. In this paper, a meta-analysis is performed and relevant investigation results from previous literature are integrated. Twenty-one works of literature published between 2000 and 2016, which comprise 27 analyses with a total samples’ size of 35,891, meet our screening conditions. Whether the relationship between alcohol consumption and telomere length is significant, this issue varies with study type (cohort, case-control, or cross-sectional) and study population (Europe, Asia, American, or Australia). It is deduced by combined evidence that alcohol consumption is associated with telomere length (with Fisher’s combined p-value = 3.52E-8 and Liptak’s weighted p-value = 8.24E-3). In the future, the consistent standardised quantifications of alcohol consumption and telomere length will avail further aggregation of the evidence from various studies. Copyright © 2021 Inderscience Enterprises Ltd.
关键词 ：
Chromosomes Chromosomes Screening Screening
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| GB/T 7714 | Li, Jianqiang , Guan, Yu , Xu, Xi et al. Association between alcohol consumption and telomere length [J]. | International Journal of Web and Grid Services , 2021 , 17 (1) : 36-59 . |
| MLA | Li, Jianqiang et al. "Association between alcohol consumption and telomere length" . | International Journal of Web and Grid Services 17 . 1 (2021) : 36-59 . |
| APA | Li, Jianqiang , Guan, Yu , Xu, Xi , Pei, Yan , Hung, Jason C. , Qiu, Weiliang . Association between alcohol consumption and telomere length . | International Journal of Web and Grid Services , 2021 , 17 (1) , 36-59 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The Internet of Things (IoT) has developed a well-defined infrastructure due to commercializing novel technologies. IoT networks enable smart devices to compile environmental information and transmit it to demanding users through an IoT gateway. The explosive increase of IoT users and sensors causes network bottlenecks, leading to significant energy depletion in IoT devices. The wireless network is a robust, empirically significant, and IoT layer based on progressive characteristics. The development of energy-efficient routing protocols for learning purposes is critical due to environmental volatility, unpredictability, and randomness in the wireless network's weight distribution. To achieve this critical need, learning-based routing systems are emerging as potential candidates due to their high degree of flexibility and accuracy. However, routing becomes more challenging in dynamic IoT networks due to the time-varying characteristics of link connections and access status. Hence, modern learning-based routing systems must be capable of adapting in real-time to network changes. This research presents an intelligent fault detection, energy-efficient, quality-of-service routing technique based on reinforcement learning to find the optimum route with the least amount of end-to-end latency. However, the cluster head selection is dependent on residual energy from the cluster nodes that reduce the entire network's existence. Consequently, it extends the network's lifetime, overcomes the data transmission's energy usage, and improves network robustness. The experimental results indicate that network efficiency has been successfully enhanced by fault-tolerance strategies that include highly trusted computing capabilities, thus decreasing the risk of network failure.
关键词 ：
Cluster head Cluster head Energy efficient Energy efficient Fault tolerant Fault tolerant Internet of things Internet of things Reinforcement learning Reinforcement learning Wireless sensor networks Wireless sensor networks
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| GB/T 7714 | Mahmood, Tariq , Li, Jianqiang , Pei, Yan et al. An intelligent fault detection approach based on reinforcement learning system in wireless sensor network [J]. | JOURNAL OF SUPERCOMPUTING , 2021 . |
| MLA | Mahmood, Tariq et al. "An intelligent fault detection approach based on reinforcement learning system in wireless sensor network" . | JOURNAL OF SUPERCOMPUTING (2021) . |
| APA | Mahmood, Tariq , Li, Jianqiang , Pei, Yan , Akhtar, Faheem , Butt, Suhail Ashfaq , Ditta, Allah et al. An intelligent fault detection approach based on reinforcement learning system in wireless sensor network . | JOURNAL OF SUPERCOMPUTING , 2021 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object's location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.
关键词 ：
breast cancer breast cancer fully connected depthwise convolutional neural network fully connected depthwise convolutional neural network image processing image processing microcalcification detection microcalcification detection
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| GB/T 7714 | Rehman, Khalil Ur , Li, Jianqiang , Pei, Yan et al. Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network [J]. | SENSORS , 2021 , 21 (14) . |
| MLA | Rehman, Khalil Ur et al. "Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network" . | SENSORS 21 . 14 (2021) . |
| APA | Rehman, Khalil Ur , Li, Jianqiang , Pei, Yan , Yasin, Anaa , Ali, Saqib , Mahmood, Tariq . Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network . | SENSORS , 2021 , 21 (14) . |
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