Sensitivity analysis （SA） is a critical part of modeling any biological system due to the inherent uncertainty in model output, as introduced by parameter values that have not been experimentally determined. SA ther...Sensitivity analysis （SA） is a critical part of modeling any biological system due to the inherent uncertainty in model output, as introduced by parameter values that have not been experimentally determined. SA therefore provides deeper understanding of the system by painting a picture of the extent to which certain model outputs vary in rela- tionship to changes in model parameters. Here we explore two types of global SA for recently developed models of nascent focal adhesion formation, a key step in cellular movement. While many SA methods have been used for deterministic methods, we uti- lize methods for both stochastic and deterministic models, providing a more complete description of the parameters to which the focal adhesion model is most sensitive. Spe- cific recommendations for further experimentation in the process of cellular motility are proposed in response to the SA.展开更多
Automatic identification and segmentation of lesions in medical images has become a focus area for researchers.Segmentation for medical image provides professionals with a clearer and more detailed view by accurately ...Automatic identification and segmentation of lesions in medical images has become a focus area for researchers.Segmentation for medical image provides professionals with a clearer and more detailed view by accurately identifying and isolating specific tissues,organs,or lesions from complex medical images,which is crucial for early diagnosis of diseases,treatment planning,and efficacy tracking.This paper introduces a deep network based on dendritic learning and missing region detection(DMNet),a new approach to medical image segmentation.DMNet combines a dendritic neuron model(DNM)with an improved SegNet framework to improve segmentation accuracy,especially in challenging tasks such as breast lesion and COVID-19 CT scan analysis.This work provides a new approach to medical image segmentation and confirms its effectiveness.Experiments have demonstrated that DMNet outperforms classic and latest methods in various performance metrics,proving its effectiveness and stability in medical image segmentation tasks.展开更多
文摘Sensitivity analysis （SA） is a critical part of modeling any biological system due to the inherent uncertainty in model output, as introduced by parameter values that have not been experimentally determined. SA therefore provides deeper understanding of the system by painting a picture of the extent to which certain model outputs vary in rela- tionship to changes in model parameters. Here we explore two types of global SA for recently developed models of nascent focal adhesion formation, a key step in cellular movement. While many SA methods have been used for deterministic methods, we uti- lize methods for both stochastic and deterministic models, providing a more complete description of the parameters to which the focal adhesion model is most sensitive. Spe- cific recommendations for further experimentation in the process of cellular motility are proposed in response to the SA.
基金supported by the Japan Society for the Promotion of Science(JSPS)KAKENHI under Grant JP22H03643Japan Science and Technology Agency(JST)Support for Pioneering Research Initiated by the Next Generation(SPRING)under Grant JPMJSP2145JST through the Establishment of University Fellowships towards the Creation of Science Technology Innovation under Grant JPMJFS2115.
文摘Automatic identification and segmentation of lesions in medical images has become a focus area for researchers.Segmentation for medical image provides professionals with a clearer and more detailed view by accurately identifying and isolating specific tissues,organs,or lesions from complex medical images,which is crucial for early diagnosis of diseases,treatment planning,and efficacy tracking.This paper introduces a deep network based on dendritic learning and missing region detection(DMNet),a new approach to medical image segmentation.DMNet combines a dendritic neuron model(DNM)with an improved SegNet framework to improve segmentation accuracy,especially in challenging tasks such as breast lesion and COVID-19 CT scan analysis.This work provides a new approach to medical image segmentation and confirms its effectiveness.Experiments have demonstrated that DMNet outperforms classic and latest methods in various performance metrics,proving its effectiveness and stability in medical image segmentation tasks.
