Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific ...Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.展开更多
由于患者个体差异、采集协议多样性和数据损坏等因素,现有基于磁共振成像(Magnetic resonance imaging,MRI)的脑肿瘤分割方法存在模态数据丢失问题,导致分割精度不高。为此,本文提出了一种基于U-Net和Transformer结合的不完整多模态脑...由于患者个体差异、采集协议多样性和数据损坏等因素,现有基于磁共振成像(Magnetic resonance imaging,MRI)的脑肿瘤分割方法存在模态数据丢失问题,导致分割精度不高。为此,本文提出了一种基于U-Net和Transformer结合的不完整多模态脑肿瘤分割(Incomplete multimodal brain tumor segmentation based on the combination of U-Net and Transformer,IM TransNet)方法。首先,针对脑肿瘤MRI的4个不同模态设计了单模态特定编码器,提升模型对各模态数据的表征能力。其次,在U-Net中嵌入双重注意力的Transformer模块,克服模态缺失引起的信息不完整问题,减少U-Net的长距离上下文交互和空间依赖性局限。在U-Net的跳跃连接中加入跳跃交叉注意力机制,动态关注不同层级和模态的特征,即使在模态缺失时,也能有效融合特征并进行重建。此外,针对模态缺失引起的训练不平衡问题,设计了辅助解码模块,确保模型在各种不完整模态子集上均能稳定高效地分割脑肿瘤。最后,基于公开数据集BRATS验证模型的性能。实验结果表明,本文提出的模型在增强型肿瘤、肿瘤核心和全肿瘤上的平均Dice评分分别为63.19%、76.42%和86.16%,证明了其在处理不完整多模态数据时的优越性和稳定性,为临床实践中脑肿瘤的准确、高效和可靠分割提供了一种可行的技术手段。展开更多
基金Project(2020YFC2008605)supported by the National Key Research and Development Project of ChinaProject(52072412)supported by the National Natural Science Foundation of ChinaProject(2021JJ30359)supported by the Natural Science Foundation of Hunan Province,China。
文摘Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.
文摘由于患者个体差异、采集协议多样性和数据损坏等因素,现有基于磁共振成像(Magnetic resonance imaging,MRI)的脑肿瘤分割方法存在模态数据丢失问题,导致分割精度不高。为此,本文提出了一种基于U-Net和Transformer结合的不完整多模态脑肿瘤分割(Incomplete multimodal brain tumor segmentation based on the combination of U-Net and Transformer,IM TransNet)方法。首先,针对脑肿瘤MRI的4个不同模态设计了单模态特定编码器,提升模型对各模态数据的表征能力。其次,在U-Net中嵌入双重注意力的Transformer模块,克服模态缺失引起的信息不完整问题,减少U-Net的长距离上下文交互和空间依赖性局限。在U-Net的跳跃连接中加入跳跃交叉注意力机制,动态关注不同层级和模态的特征,即使在模态缺失时,也能有效融合特征并进行重建。此外,针对模态缺失引起的训练不平衡问题,设计了辅助解码模块,确保模型在各种不完整模态子集上均能稳定高效地分割脑肿瘤。最后,基于公开数据集BRATS验证模型的性能。实验结果表明,本文提出的模型在增强型肿瘤、肿瘤核心和全肿瘤上的平均Dice评分分别为63.19%、76.42%和86.16%,证明了其在处理不完整多模态数据时的优越性和稳定性,为临床实践中脑肿瘤的准确、高效和可靠分割提供了一种可行的技术手段。
