We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adapt...We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adaptive semantic transmission in terms of different channel states.We combine the classic broadcast approach with the image transformer to implement this adaptive joint source and channel coding(JSCC)scheme.Specifically,we utilize the neural network(NN)to jointly optimize the hierarchical image compression and superposition code mapping within this scheme.The learned transformers and codebooks allow recovering of the image with an adaptive quality and low error rate at the receiver side,in each channel state.The simulation results exhibit our proposed scheme can dynamically adapt the coding to the current channel state and outperform some existing intelligent schemes with the fixed coding block.展开更多
目的探讨基于多期相动态对比增强磁共振(dynamic contrast-enhanced magnetic resonance imaging,DCE-MRI)影像组学在预测浸润性乳腺癌前哨淋巴结(sentinel lymph node,SLN)转移中的价值。方法回顾性收集2018年11月至2021年9月在济宁市...目的探讨基于多期相动态对比增强磁共振(dynamic contrast-enhanced magnetic resonance imaging,DCE-MRI)影像组学在预测浸润性乳腺癌前哨淋巴结(sentinel lymph node,SLN)转移中的价值。方法回顾性收集2018年11月至2021年9月在济宁市第一人民医院术前接受乳腺DCE-MRI检查且经病理证实的150名浸润性乳腺癌患者的临床、病理及MRI资料,其中,SLN转移阳性者61名,阴性者89名,并将其以8∶2的比例随机划分为训练集(n=120)与测试集(n=30)。在乳腺DCE-MRI(增强早期、增强峰值期及增强末期)剪影图像上进行手动逐层勾画感兴趣区(region of interest,ROI),获得三维容积感兴趣区域(volume of interest,VOI),再对各期进行提取影像组学特征,使用Z分数(Z-Score)归一化对特征进行归一化处理,然后再使用Select K Best和最小绝对收缩与选择算法(least absolute shrinkage and selection operator,LASSO)筛选出最优特征,并构建logistic回归(logistic regression,LR)模型。绘制受试者工作特征(receiver operating characteristic,ROC)曲线及曲线下面积(area under the curve,AUC)。运用ROC曲线与决策曲线分析(decision curve analysis,DCA)对模型进行评价。结果分别从增强早期、增强峰值期、增强末期及三期联合期相的图像中得到了10、10、10及11个最优特征,通过LR共构建4个预测模型。在训练集中,4个模型的AUC值分别为0.859、0.801、0.768、0.834。在测试集中,4个模型AUC值分别为0.843、0.806、0.806、0.866。DCA显示联合期相模型表现出了较高的净收益。结论DCE-MRI增强早期、增强峰值期及增强末期影像组学模型在预测浸润性乳腺癌SLN转移中均具有较好的预测效能,且测试集中联合期相的效能略高于单独期相。展开更多
In view of the problems of multi-scale changes of segmentation targets,noise interference,rough segmentation results and slow training process faced by medical image semantic segmentation,a multi-scale residual aggreg...In view of the problems of multi-scale changes of segmentation targets,noise interference,rough segmentation results and slow training process faced by medical image semantic segmentation,a multi-scale residual aggregation U-shaped attention network structure of MAAUNet(MultiRes aggregation attention UNet)is proposed based on MultiResUNet.Firstly,aggregate connection is introduced from the original feature aggregation at the same level.Skip connection is redesigned to aggregate features of different semantic scales at the decoder subnet,and the problem of semantic gaps is further solved that may exist between skip connections.Secondly,after the multi-scale convolution module,a convolution block attention module is added to focus and integrate features in the two attention directions of channel and space to adaptively optimize the intermediate feature map.Finally,the original convolution block is improved.The convolution channels are expanded with a series convolution structure to complement each other and extract richer spatial features.Residual connections are retained and the convolution block is turned into a multi-channel convolution block.The model is made to extract multi-scale spatial features.The experimental results show that MAAUNet has strong competitiveness in challenging datasets,and shows good segmentation performance and stability in dealing with multi-scale input and noise interference.展开更多
目的探讨基于增强CT动脉期、静脉期及动静脉期联合的直方图分析在鉴别胃肠道间质瘤和神经鞘瘤的诊断价值。方法回顾性分析行腹部多期增强CT扫描且经手术病理证实的40例胃肠道间质瘤及19例胃肠道神经鞘瘤患者的临床及影像学资料。在uAI R...目的探讨基于增强CT动脉期、静脉期及动静脉期联合的直方图分析在鉴别胃肠道间质瘤和神经鞘瘤的诊断价值。方法回顾性分析行腹部多期增强CT扫描且经手术病理证实的40例胃肠道间质瘤及19例胃肠道神经鞘瘤患者的临床及影像学资料。在uAI Research Portal平台上手动勾画肿瘤感兴趣区并进行直方图分析,获得肿瘤动脉期、静脉期及动静脉期联合直方图特征参数,对各参数进行比较并研究各参数的诊断效能。结果动脉期的直方图特征参数,90%像素值、熵、四分位距、最大值、平均偏差、范围、相对偏差、偏度、均匀性差异具有统计学意义,静脉期的直方图特征参数,熵、四分位距、最大值、平均偏差、相对偏差、均匀性、方差差异具有统计学意义。分别建立动脉期、静脉期及动静脉期联合的鉴别诊断模型,得出动脉期(AUC=0.855)及动静脉期联合(AUC=0.899)诊断效能均高于静脉期模型(AUC=0.751)。结论基于多期增强CT的直方图分析可用于术前鉴别胃肠道间质瘤和神经鞘瘤,动脉期及动静脉期联合的诊断效能更高。展开更多
基金supported in part by the National Key R&D Project of China under Grant 2020YFA0712300National Natural Science Foundation of China under Grant NSFC-62231022,12031011supported in part by the NSF of China under Grant 62125108。
文摘We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adaptive semantic transmission in terms of different channel states.We combine the classic broadcast approach with the image transformer to implement this adaptive joint source and channel coding(JSCC)scheme.Specifically,we utilize the neural network(NN)to jointly optimize the hierarchical image compression and superposition code mapping within this scheme.The learned transformers and codebooks allow recovering of the image with an adaptive quality and low error rate at the receiver side,in each channel state.The simulation results exhibit our proposed scheme can dynamically adapt the coding to the current channel state and outperform some existing intelligent schemes with the fixed coding block.
文摘目的探讨基于表观弥散系数(apparent diffusion coefficient,ADC)图、T1WI及T2WI序列构建的影像组学模型鉴别唾液腺多形性腺瘤(pleomorphic adenoma,PA)和基底细胞腺瘤(basal cell adenoma,BCA)的价值。材料与方法回顾性分析2015年1月至2021年10月来自济宁市第一人民医院的唾液腺129例PA和48例BCA患者的MR图像,并将其以8∶2的比例随机划分为训练集(n=141)与测试集(n=36)。在横断位ADC、T1WI及T2WI图像上手动勾画肿瘤的三维容积感兴趣区域,提取影像组学特征;采用方差阈值法、方差分析(analysis of variance,ANOVA)及基于5折交叉验证的最小绝对收缩与选择算法(least absolute shrinkage and selection operator,LASSO)筛选最有价值的特征,将筛选出的特征结合逻辑回归(logistic regression,LR)与支持向量机(support vector machine,SVM)两种分类器后进行模型训练,并在测试集中验证。绘制ROC曲线来评估LR模型与SVM模型鉴别PA和BCA的效能。此外,使用Delong Test对模型进行比较,使用决策曲线及校准曲线对模型进行评价。结果分别从ADC、T1WI、T2WI及联合序列(ADC+T1WI+T2WI)图像中得到15、3、15及23个最优特征。在训练集中,基于ADC图、T1WI图、T2WI图、联合模型构建的LR与SVM模型的曲线下面积(area under the curve,AUC)分别为0.955、0.961、0.812、0.813、0.939、0.949、0.994、0.995;基于ADC、T1WI、T2WI及联合序列图像构建的LR模型鉴别诊断PA和BCA的AUC值分别为0.906、0.780、0.868及0.972,SVM模型的AUC值分别为0.924、0.783、0.847及0.959;在训练集中,基于联合序列模型优于基于T1WI或T2WI影像组学模型(P<0.05),与基于ADC影像组学模型差异无统计学意义(P>0.05),联合序列模型的准确率、敏感度及特异度分别为98.6%~98.7%、96.4%~98.4%、98.8%~99.4%,ADC影像组学模型的准确率、敏感度及特异度分别为91.4%~91.8%、75.0%~79.7%、95.7%~98.1%;在测试集中,各模型间的AUC值均无显著性差异(P>0.05)。结论多序列联合模型及ADC影像组学模型鉴别多形性腺瘤和基底细胞腺瘤优于T1WI及T2WI序列,且与ADC影像组学模型比较,联合序列模型具有较高的准确率、敏感度及特异度。
文摘目的探讨基于多期相动态对比增强磁共振(dynamic contrast-enhanced magnetic resonance imaging,DCE-MRI)影像组学在预测浸润性乳腺癌前哨淋巴结(sentinel lymph node,SLN)转移中的价值。方法回顾性收集2018年11月至2021年9月在济宁市第一人民医院术前接受乳腺DCE-MRI检查且经病理证实的150名浸润性乳腺癌患者的临床、病理及MRI资料,其中,SLN转移阳性者61名,阴性者89名,并将其以8∶2的比例随机划分为训练集(n=120)与测试集(n=30)。在乳腺DCE-MRI(增强早期、增强峰值期及增强末期)剪影图像上进行手动逐层勾画感兴趣区(region of interest,ROI),获得三维容积感兴趣区域(volume of interest,VOI),再对各期进行提取影像组学特征,使用Z分数(Z-Score)归一化对特征进行归一化处理,然后再使用Select K Best和最小绝对收缩与选择算法(least absolute shrinkage and selection operator,LASSO)筛选出最优特征,并构建logistic回归(logistic regression,LR)模型。绘制受试者工作特征(receiver operating characteristic,ROC)曲线及曲线下面积(area under the curve,AUC)。运用ROC曲线与决策曲线分析(decision curve analysis,DCA)对模型进行评价。结果分别从增强早期、增强峰值期、增强末期及三期联合期相的图像中得到了10、10、10及11个最优特征,通过LR共构建4个预测模型。在训练集中,4个模型的AUC值分别为0.859、0.801、0.768、0.834。在测试集中,4个模型AUC值分别为0.843、0.806、0.806、0.866。DCA显示联合期相模型表现出了较高的净收益。结论DCE-MRI增强早期、增强峰值期及增强末期影像组学模型在预测浸润性乳腺癌SLN转移中均具有较好的预测效能,且测试集中联合期相的效能略高于单独期相。
基金National Natural Science Foundation of China(No.61806006)Jiangsu University Superior Discipline Construction Project。
文摘In view of the problems of multi-scale changes of segmentation targets,noise interference,rough segmentation results and slow training process faced by medical image semantic segmentation,a multi-scale residual aggregation U-shaped attention network structure of MAAUNet(MultiRes aggregation attention UNet)is proposed based on MultiResUNet.Firstly,aggregate connection is introduced from the original feature aggregation at the same level.Skip connection is redesigned to aggregate features of different semantic scales at the decoder subnet,and the problem of semantic gaps is further solved that may exist between skip connections.Secondly,after the multi-scale convolution module,a convolution block attention module is added to focus and integrate features in the two attention directions of channel and space to adaptively optimize the intermediate feature map.Finally,the original convolution block is improved.The convolution channels are expanded with a series convolution structure to complement each other and extract richer spatial features.Residual connections are retained and the convolution block is turned into a multi-channel convolution block.The model is made to extract multi-scale spatial features.The experimental results show that MAAUNet has strong competitiveness in challenging datasets,and shows good segmentation performance and stability in dealing with multi-scale input and noise interference.
文摘目的探讨基于增强CT动脉期、静脉期及动静脉期联合的直方图分析在鉴别胃肠道间质瘤和神经鞘瘤的诊断价值。方法回顾性分析行腹部多期增强CT扫描且经手术病理证实的40例胃肠道间质瘤及19例胃肠道神经鞘瘤患者的临床及影像学资料。在uAI Research Portal平台上手动勾画肿瘤感兴趣区并进行直方图分析,获得肿瘤动脉期、静脉期及动静脉期联合直方图特征参数,对各参数进行比较并研究各参数的诊断效能。结果动脉期的直方图特征参数,90%像素值、熵、四分位距、最大值、平均偏差、范围、相对偏差、偏度、均匀性差异具有统计学意义,静脉期的直方图特征参数,熵、四分位距、最大值、平均偏差、相对偏差、均匀性、方差差异具有统计学意义。分别建立动脉期、静脉期及动静脉期联合的鉴别诊断模型,得出动脉期(AUC=0.855)及动静脉期联合(AUC=0.899)诊断效能均高于静脉期模型(AUC=0.751)。结论基于多期增强CT的直方图分析可用于术前鉴别胃肠道间质瘤和神经鞘瘤,动脉期及动静脉期联合的诊断效能更高。
