Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiote...Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.展开更多
医学图像分割在计算机辅助诊断和手术导航等临床应用中起着至关重要的作用,旨在从复杂的医学影像中精准提取不同器官和病灶。然而,现有的U型网络结构在实际应用中存在跳跃连接信息冗余大和计算量高等问题。为了解决这些问题,提出一种轻...医学图像分割在计算机辅助诊断和手术导航等临床应用中起着至关重要的作用,旨在从复杂的医学影像中精准提取不同器官和病灶。然而,现有的U型网络结构在实际应用中存在跳跃连接信息冗余大和计算量高等问题。为了解决这些问题,提出一种轻量化医学图像分割网络ES-TransUNet(Efficient channel attention and Simple-TransUNet)。该网络在编码器中通过引入十字交叉注意力(CCA)机制捕捉图像中的长距离依赖关系,并优化Transformer中的多头注意力结构,从而使模型轻量化,在解码器中引入动态上采样(Dysample)模块提升上采样效率;同时为了减少跳跃连接中的信息冗余,引入简单上下文Transformer(SCOT)块对冗余特征进行过滤。在Synapse多器官分割和ACDC数据集上的实验结果表明,ES-TransUNet相比TransUNet分别取得了2.37和1.57个百分点的Dice相似系数(DSC)提升,并在Synapse数据集上使Hausdorff距离(HD)降低了约9.69。此外,所提网络与现有最先进的医学分割模型的对比结果表明,ES-TransUNet在保持较高分割精度的基础上,显著降低了模型的参数量和计算复杂度,并提高了推理效率。可见,该网络更满足实时医学图像分割的实际需求。展开更多
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]an Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]+3 种基金a project supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209]a Research Council of Norway funded project(MAPARC)[grant number 328943]a Nansen Center´s basic institutional funding[grant number 342624]the high-performance computing support from the School of Atmospheric Science at Sun Yat-sen University。
文摘Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.
文摘医学图像分割在计算机辅助诊断和手术导航等临床应用中起着至关重要的作用,旨在从复杂的医学影像中精准提取不同器官和病灶。然而,现有的U型网络结构在实际应用中存在跳跃连接信息冗余大和计算量高等问题。为了解决这些问题,提出一种轻量化医学图像分割网络ES-TransUNet(Efficient channel attention and Simple-TransUNet)。该网络在编码器中通过引入十字交叉注意力(CCA)机制捕捉图像中的长距离依赖关系,并优化Transformer中的多头注意力结构,从而使模型轻量化,在解码器中引入动态上采样(Dysample)模块提升上采样效率;同时为了减少跳跃连接中的信息冗余,引入简单上下文Transformer(SCOT)块对冗余特征进行过滤。在Synapse多器官分割和ACDC数据集上的实验结果表明,ES-TransUNet相比TransUNet分别取得了2.37和1.57个百分点的Dice相似系数(DSC)提升,并在Synapse数据集上使Hausdorff距离(HD)降低了约9.69。此外,所提网络与现有最先进的医学分割模型的对比结果表明,ES-TransUNet在保持较高分割精度的基础上,显著降低了模型的参数量和计算复杂度,并提高了推理效率。可见,该网络更满足实时医学图像分割的实际需求。
