目的:实现医疗设备维修精细化管理,有效提高工作效率,节约设备维护成本。方法:在医院资源规划(hospital resource planning,HRP)系统下,利用SOL Server 2008平台建立医疗设备维修模块,对设备验收、维护保养、处置等过程进行信息化管理...目的:实现医疗设备维修精细化管理,有效提高工作效率,节约设备维护成本。方法:在医院资源规划(hospital resource planning,HRP)系统下,利用SOL Server 2008平台建立医疗设备维修模块,对设备验收、维护保养、处置等过程进行信息化管理。结果:医疗设备维修管理模块的应用,实现了精确管理,有效控制了成本,从而提高了医院整体社会效益和经济效益。结论:医疗设备维修管理模块是医疗设备维修管理信息化的一部分,有助于实现医院持续改进。展开更多
Deep learning techniques are revolutionizing the developmentof medical image segmentation.With the advancement of Transformer models,especially ViT and Swin-Transformer,which enhances the remote-dependent modeling cap...Deep learning techniques are revolutionizing the developmentof medical image segmentation.With the advancement of Transformer models,especially ViT and Swin-Transformer,which enhances the remote-dependent modeling capability of the model through the self-attention mechanism,better segmentation performance can be achieve.Moreover,the high computational cost of Transformer has motivated researchers to explore more efficient models,such as the Mamba model based on state-space modeling(SSM),and for the field of medical segmentation,reducing the number of model parameters is also necessary.In this study,a novel asymmetric model called LA-UMamba was proposed,which integrates visual Mamba module to efficiently capture complex visual features and remote dependencies.The classical design of U-Net was adopted in the upsampling phase to help reduce the number of references and recover more details.To mitigate the information loss problem,an auxiliary U-Net downsampling layer was designed to focus on sizing without extracting features,thus enhancing the protection of input information while maintaining the efficiency of the model.The experiments were conducted on the ACDC MRI cardiac segmentation dataset,and the results showed that the proposed LA-UMamba achieves proved performance compared to the baseline model in several evaluation metrics,such as IoU,Accuracy,Precision,HD and ASD,which improved that the model is successful in optimizing the detail processing and reducing the complexity of the model,providing a new perspective for further optimization of medical image segmentation techniques.展开更多
文摘目的:实现医疗设备维修精细化管理,有效提高工作效率,节约设备维护成本。方法:在医院资源规划(hospital resource planning,HRP)系统下,利用SOL Server 2008平台建立医疗设备维修模块,对设备验收、维护保养、处置等过程进行信息化管理。结果:医疗设备维修管理模块的应用,实现了精确管理,有效控制了成本,从而提高了医院整体社会效益和经济效益。结论:医疗设备维修管理模块是医疗设备维修管理信息化的一部分,有助于实现医院持续改进。
文摘Deep learning techniques are revolutionizing the developmentof medical image segmentation.With the advancement of Transformer models,especially ViT and Swin-Transformer,which enhances the remote-dependent modeling capability of the model through the self-attention mechanism,better segmentation performance can be achieve.Moreover,the high computational cost of Transformer has motivated researchers to explore more efficient models,such as the Mamba model based on state-space modeling(SSM),and for the field of medical segmentation,reducing the number of model parameters is also necessary.In this study,a novel asymmetric model called LA-UMamba was proposed,which integrates visual Mamba module to efficiently capture complex visual features and remote dependencies.The classical design of U-Net was adopted in the upsampling phase to help reduce the number of references and recover more details.To mitigate the information loss problem,an auxiliary U-Net downsampling layer was designed to focus on sizing without extracting features,thus enhancing the protection of input information while maintaining the efficiency of the model.The experiments were conducted on the ACDC MRI cardiac segmentation dataset,and the results showed that the proposed LA-UMamba achieves proved performance compared to the baseline model in several evaluation metrics,such as IoU,Accuracy,Precision,HD and ASD,which improved that the model is successful in optimizing the detail processing and reducing the complexity of the model,providing a new perspective for further optimization of medical image segmentation techniques.
