Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were col...Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.展开更多
盐体是具有良好气密性的地质构造,有利于油气储存,实现精细化盐体的解释极为必要。然而,不同于断层,盐体的特征较为复杂且形态差异大,常规方法易导致混淆和误判。此外,基于数据驱动的盐体识别模型在实际数据集上的泛化能力较差,因此目...盐体是具有良好气密性的地质构造,有利于油气储存,实现精细化盐体的解释极为必要。然而,不同于断层,盐体的特征较为复杂且形态差异大,常规方法易导致混淆和误判。此外,基于数据驱动的盐体识别模型在实际数据集上的泛化能力较差,因此目前在地震勘探中进行盐体的解释及可视化仍存在挑战。文章将盐体解释视为地震图像的语义分割问题,提出了基于迁移学习的上下文融合与混合注意力的智能盐体分割(Multi-path structure Mixed Attention and Transfer Optimized Net,MMTONet)方法。同时设计了一种基于盐体上下文特征融合模块,进而建立了改进注意力卷积混合的跳跃连接机制,以更好地弥补由下采样造成的信息损失,从而提高模型对盐体边界与高振幅噪声的像素级辨别能力。在此基础上,还设计了迁移学习的适配器微调策略,提升了模型在实际数据上的泛化能力。在地震数据集上的实验结果表明,MMTONet在提高分割精度和减少计算量、参数量方面均优于主流的语义分割方法。展开更多
本文论述了Learning by Doing,Learning by Abstracting,Learning by Analogy,Learning byTeaching以及Learing by Simulating等教学模式。分析了其优点,并介绍了我们将其应用于嵌入式软件开发导论和系统结构等课程的效果。文中尤其强调...本文论述了Learning by Doing,Learning by Abstracting,Learning by Analogy,Learning byTeaching以及Learing by Simulating等教学模式。分析了其优点,并介绍了我们将其应用于嵌入式软件开发导论和系统结构等课程的效果。文中尤其强调了Learning by Abstracting的重要性。展开更多
血液系统肿瘤作为多病灶的全身性肿瘤,早期病灶筛查和诊断对治疗和预后至关重要。全身磁共振成像(whole-body magnetic resonance imaging,WB-MRI)作为一种无电离辐射、软组织分辨率高的全身成像技术,对血液系统肿瘤早期病变,包括微小...血液系统肿瘤作为多病灶的全身性肿瘤,早期病灶筛查和诊断对治疗和预后至关重要。全身磁共振成像(whole-body magnetic resonance imaging,WB-MRI)作为一种无电离辐射、软组织分辨率高的全身成像技术,对血液系统肿瘤早期病变,包括微小病灶和转移病灶的检测具有优势,已逐步应用于血液系统肿瘤的早期筛查、诊断分期、治疗反应评估及复发预测等方面。本文就WB-MRI在血液系统肿瘤,包括多发性骨髓瘤(multiple myeloma,MM)、淋巴瘤和白血病中的诊疗及预后进展予以综述,并分析这三种血液系统肿瘤在WB-MRI上的异同点,探讨机器学习和深度学习技术在WB-MRI的应用,旨在为临床诊治血液系统肿瘤和今后的研究提供参考。展开更多
针对步态识别过程易受拍摄视角、外观变化等因素影响问题,提出一种融合点云步态模型与深度学习的步态识别算法。算法通过轻量级特征描述符(lightweight feature descriptor,LFD)提取图像特征,并将其进行特征配准;基于几何-匹配核预处理...针对步态识别过程易受拍摄视角、外观变化等因素影响问题,提出一种融合点云步态模型与深度学习的步态识别算法。算法通过轻量级特征描述符(lightweight feature descriptor,LFD)提取图像特征,并将其进行特征配准;基于几何-匹配核预处理增强识别技术(gait model-key point recognition and extraction,GM-KPRE)提取人体关键点信息,在支持向量机算法中引入径向基函数核进行步态分类和识别;在公开数据集CASIA-B和Market-1501-v15.09.15上进行实验验证,实验结果表明,算法能有效提高步态识别准确率和效率。展开更多
文摘Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.
文摘盐体是具有良好气密性的地质构造,有利于油气储存,实现精细化盐体的解释极为必要。然而,不同于断层,盐体的特征较为复杂且形态差异大,常规方法易导致混淆和误判。此外,基于数据驱动的盐体识别模型在实际数据集上的泛化能力较差,因此目前在地震勘探中进行盐体的解释及可视化仍存在挑战。文章将盐体解释视为地震图像的语义分割问题,提出了基于迁移学习的上下文融合与混合注意力的智能盐体分割(Multi-path structure Mixed Attention and Transfer Optimized Net,MMTONet)方法。同时设计了一种基于盐体上下文特征融合模块,进而建立了改进注意力卷积混合的跳跃连接机制,以更好地弥补由下采样造成的信息损失,从而提高模型对盐体边界与高振幅噪声的像素级辨别能力。在此基础上,还设计了迁移学习的适配器微调策略,提升了模型在实际数据上的泛化能力。在地震数据集上的实验结果表明,MMTONet在提高分割精度和减少计算量、参数量方面均优于主流的语义分割方法。
文摘本文论述了Learning by Doing,Learning by Abstracting,Learning by Analogy,Learning byTeaching以及Learing by Simulating等教学模式。分析了其优点,并介绍了我们将其应用于嵌入式软件开发导论和系统结构等课程的效果。文中尤其强调了Learning by Abstracting的重要性。
文摘血液系统肿瘤作为多病灶的全身性肿瘤,早期病灶筛查和诊断对治疗和预后至关重要。全身磁共振成像(whole-body magnetic resonance imaging,WB-MRI)作为一种无电离辐射、软组织分辨率高的全身成像技术,对血液系统肿瘤早期病变,包括微小病灶和转移病灶的检测具有优势,已逐步应用于血液系统肿瘤的早期筛查、诊断分期、治疗反应评估及复发预测等方面。本文就WB-MRI在血液系统肿瘤,包括多发性骨髓瘤(multiple myeloma,MM)、淋巴瘤和白血病中的诊疗及预后进展予以综述,并分析这三种血液系统肿瘤在WB-MRI上的异同点,探讨机器学习和深度学习技术在WB-MRI的应用,旨在为临床诊治血液系统肿瘤和今后的研究提供参考。
文摘针对步态识别过程易受拍摄视角、外观变化等因素影响问题,提出一种融合点云步态模型与深度学习的步态识别算法。算法通过轻量级特征描述符(lightweight feature descriptor,LFD)提取图像特征,并将其进行特征配准;基于几何-匹配核预处理增强识别技术(gait model-key point recognition and extraction,GM-KPRE)提取人体关键点信息,在支持向量机算法中引入径向基函数核进行步态分类和识别;在公开数据集CASIA-B和Market-1501-v15.09.15上进行实验验证,实验结果表明,算法能有效提高步态识别准确率和效率。