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
Adequate care of type 2 diabetes is reflected by the individual’s adherence to dietary guidance;yet, few patients are engaged in diabetes self-care at the recommended level, regardless of race/ethnicity. Few studies ...Adequate care of type 2 diabetes is reflected by the individual’s adherence to dietary guidance;yet, few patients are engaged in diabetes self-care at the recommended level, regardless of race/ethnicity. Few studies on the effect of dietary medical advice on diabetes self-management (DSM) and glycemic control have been conducted on Haitian and African American adults with type 2 diabetes. These relationships were assessed in total of 254 Blacks with type 2 diabetes (Haitian Americans = 129;African Americans = 125) recruited from Miami-Dade and Broward Counties, Florida by community outreach methods. Although dietary advice received was not significantly different between the two Black ethnicities, given advice “to follow a diet” as a predictor of “using food groups” was significant for Haitian Americans, but not for African Americans. Haitian Americans who were advised to follow a diet were approximately 3 times more likely to sometimes or often use food groups (or exchange lists) in planning meals. Less than optimal glycemic control (A1C > 7.2) was inversely related to DSM for African Americans;but the relationship was not significant for Haitian Americans. A one unit increase in DSM score decreased the odds ratio point estimate of having less than optimal glycemic control (A1C > 7.2%) by a factor of 0.94 in African Americans. These results suggest that medical advice for diet plans may not be communicated effectively for DSM for some races/ethnicities. Research aimed at uncovering the enablers and barriers of diet management specific to Black ethnicities with type 2 diabetes is recommended.展开更多
Using BOPPPS teaching module combined with the present situation of medical higher mathematics teaching in Guangxi University of Chinese Medicine, this paper introduces the application of this teaching method in medic...Using BOPPPS teaching module combined with the present situation of medical higher mathematics teaching in Guangxi University of Chinese Medicine, this paper introduces the application of this teaching method in medical higher mathematics teaching, and explores the thinking of teaching reform of medical higher mathematics in Guangxi University of Chinese Medicine, so as to improve classroom teaching efficiency.展开更多
Medical image classification has played an important role in the medical field, and the related method based on deep learning has become an important and powerful technique in medical image classification. In this art...Medical image classification has played an important role in the medical field, and the related method based on deep learning has become an important and powerful technique in medical image classification. In this article, we propose a simplified inception module based Hadamard attention (SI + HA) mechanism for medical image classification. Specifically, we propose a new attention mechanism: Hadamard attention mechanism. It improves the accuracy of medical image classification without greatly increasing the complexity of the model. Meanwhile, we adopt a simplified inception module to improve the utilization of parameters. We use two medical image datasets to prove the superiority of our proposed method. In the BreakHis dataset, the AUCs of our method can reach 98.74%, 98.38%, 98.61% and 97.67% under the magnification factors of 40×, 100×, 200× and 400×, respectively. The accuracies can reach 95.67%, 94.17%, 94.53% and 94.12% under the magnification factors of 40×, 100×, 200× and 400×, respectively. In the KIMIA Path 960 dataset, the AUCs and accuracy of our method can reach 99.91% and 99.03%. It is superior to the currently popular methods and can significantly improve the effectiveness of medical image classification.展开更多
To build towards expertise, one has to accept to modify his way of practicing, including： （1） a need to reflect on and about the action; （2） a continuous concern about our competence to practice; （3） tireless e...To build towards expertise, one has to accept to modify his way of practicing, including： （1） a need to reflect on and about the action; （2） a continuous concern about our competence to practice; （3） tireless effort to combine metacognition and mental practice in a trans-disciplinary approach; （4） adding research with neuroscience, understanding neuroplasticity, modulation and artificial intelligence. Usual practice actually does not include a continuous concern for CME （continued medical education） and is intermittent at best. This new paradigm constitutes the basis of our approach. Expertise starting in 2015 is described as an asymptotic curve unable to be obtained with usual practice and intermittent education. We suggest a new way of conceiving CME combining practice, reflection on action and in-situ simulation laboratory near work. We are describing TEE （technology-enhanced education） coupled with certain neuro-enhancers to achieve a break in the asymptotic curve of expertise. This is in reality a new conception of CME in medicine.展开更多
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
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.
文摘Adequate care of type 2 diabetes is reflected by the individual’s adherence to dietary guidance;yet, few patients are engaged in diabetes self-care at the recommended level, regardless of race/ethnicity. Few studies on the effect of dietary medical advice on diabetes self-management (DSM) and glycemic control have been conducted on Haitian and African American adults with type 2 diabetes. These relationships were assessed in total of 254 Blacks with type 2 diabetes (Haitian Americans = 129;African Americans = 125) recruited from Miami-Dade and Broward Counties, Florida by community outreach methods. Although dietary advice received was not significantly different between the two Black ethnicities, given advice “to follow a diet” as a predictor of “using food groups” was significant for Haitian Americans, but not for African Americans. Haitian Americans who were advised to follow a diet were approximately 3 times more likely to sometimes or often use food groups (or exchange lists) in planning meals. Less than optimal glycemic control (A1C > 7.2) was inversely related to DSM for African Americans;but the relationship was not significant for Haitian Americans. A one unit increase in DSM score decreased the odds ratio point estimate of having less than optimal glycemic control (A1C > 7.2%) by a factor of 0.94 in African Americans. These results suggest that medical advice for diet plans may not be communicated effectively for DSM for some races/ethnicities. Research aimed at uncovering the enablers and barriers of diet management specific to Black ethnicities with type 2 diabetes is recommended.
文摘Using BOPPPS teaching module combined with the present situation of medical higher mathematics teaching in Guangxi University of Chinese Medicine, this paper introduces the application of this teaching method in medical higher mathematics teaching, and explores the thinking of teaching reform of medical higher mathematics in Guangxi University of Chinese Medicine, so as to improve classroom teaching efficiency.
文摘Medical image classification has played an important role in the medical field, and the related method based on deep learning has become an important and powerful technique in medical image classification. In this article, we propose a simplified inception module based Hadamard attention (SI + HA) mechanism for medical image classification. Specifically, we propose a new attention mechanism: Hadamard attention mechanism. It improves the accuracy of medical image classification without greatly increasing the complexity of the model. Meanwhile, we adopt a simplified inception module to improve the utilization of parameters. We use two medical image datasets to prove the superiority of our proposed method. In the BreakHis dataset, the AUCs of our method can reach 98.74%, 98.38%, 98.61% and 97.67% under the magnification factors of 40×, 100×, 200× and 400×, respectively. The accuracies can reach 95.67%, 94.17%, 94.53% and 94.12% under the magnification factors of 40×, 100×, 200× and 400×, respectively. In the KIMIA Path 960 dataset, the AUCs and accuracy of our method can reach 99.91% and 99.03%. It is superior to the currently popular methods and can significantly improve the effectiveness of medical image classification.
文摘To build towards expertise, one has to accept to modify his way of practicing, including： （1） a need to reflect on and about the action; （2） a continuous concern about our competence to practice; （3） tireless effort to combine metacognition and mental practice in a trans-disciplinary approach; （4） adding research with neuroscience, understanding neuroplasticity, modulation and artificial intelligence. Usual practice actually does not include a continuous concern for CME （continued medical education） and is intermittent at best. This new paradigm constitutes the basis of our approach. Expertise starting in 2015 is described as an asymptotic curve unable to be obtained with usual practice and intermittent education. We suggest a new way of conceiving CME combining practice, reflection on action and in-situ simulation laboratory near work. We are describing TEE （technology-enhanced education） coupled with certain neuro-enhancers to achieve a break in the asymptotic curve of expertise. This is in reality a new conception of CME in medicine.
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。
