Several recent successes in deep learning(DL),such as state-of-the-art performance on several image classification benchmarks,have been achieved through the improved configuration.Hyperparameters(HPs)tuning is a key f...Several recent successes in deep learning(DL),such as state-of-the-art performance on several image classification benchmarks,have been achieved through the improved configuration.Hyperparameters(HPs)tuning is a key factor affecting the performance of machine learning(ML)algorithms.Various state-of-the-art DL models use different HPs in different ways for classification tasks on different datasets.This manuscript provides a brief overview of learning parameters and configuration techniques to show the benefits of using a large-scale handdrawn sketch dataset for classification problems.We analyzed the impact of different learning parameters and toplayer configurations with batch normalization(BN)and dropouts on the performance of the pre-trained visual geometry group 19(VGG-19).The analyzed learning parameters include different learning rates and momentum values of two different optimizers,such as stochastic gradient descent(SGD)and Adam.Our analysis demonstrates that using the SGD optimizer and learning parameters,such as small learning rates with high values of momentum,along with both BN and dropouts in top layers,has a good impact on the sketch image classification accuracy.展开更多
目的:探讨手绘导航在肺外周结节患者行支气管镜肺活检中的应用价值及其对支气管镜到达活检部位时间的影响因素。方法:回顾性分析2022年8月—2023年3月在宜昌市中心人民医院呼吸内镜中心接受径向超声引导下经支气管镜肺活检的116例肺外...目的:探讨手绘导航在肺外周结节患者行支气管镜肺活检中的应用价值及其对支气管镜到达活检部位时间的影响因素。方法:回顾性分析2022年8月—2023年3月在宜昌市中心人民医院呼吸内镜中心接受径向超声引导下经支气管镜肺活检的116例肺外周结节患者,根据术前是否采用手绘导航规划路径,分为手绘导航联合径向超声组(手绘导航+RP-EBUS-GS组,n=60)和径向超声组(RP-EBUS-GS组,n=56),对比分析手绘导航在支气管镜肺活检中的应用效果。采用单因素及多因素Logistic回归分析手绘导航对支气管镜到达活检部位时间的影响因素。结果:手绘导航+RP-EBUS-GS组支气管镜到达活检部位时间明显短于RP-EBUS-GS组(6.32±3.10 min vs 8.89±4.09 min,P<0.001)。以支气管镜到达活检部位时间为因变量,单因素分析结果显示,两组患者性别、结节与支气管关系、所在支气管分级及是否应用手绘导航有明显差异(均P<0.05);多因素Logistic结果显示,所在支气管分级及是否应用手绘导航是支气管镜到达活检部位时间的独立影响因素(均P<0.05)。结论:手绘导航在肺外周结节患者行支气管镜肺活检中可明显缩短支气管镜到达活检部位的时间,具有很好的临床应用价值。肺外周结节所在支气管分级及是否应用手绘导航是支气管镜到达肺活检部位时间的独立影响因素。展开更多
文摘Several recent successes in deep learning(DL),such as state-of-the-art performance on several image classification benchmarks,have been achieved through the improved configuration.Hyperparameters(HPs)tuning is a key factor affecting the performance of machine learning(ML)algorithms.Various state-of-the-art DL models use different HPs in different ways for classification tasks on different datasets.This manuscript provides a brief overview of learning parameters and configuration techniques to show the benefits of using a large-scale handdrawn sketch dataset for classification problems.We analyzed the impact of different learning parameters and toplayer configurations with batch normalization(BN)and dropouts on the performance of the pre-trained visual geometry group 19(VGG-19).The analyzed learning parameters include different learning rates and momentum values of two different optimizers,such as stochastic gradient descent(SGD)and Adam.Our analysis demonstrates that using the SGD optimizer and learning parameters,such as small learning rates with high values of momentum,along with both BN and dropouts in top layers,has a good impact on the sketch image classification accuracy.
文摘目的:探讨手绘导航在肺外周结节患者行支气管镜肺活检中的应用价值及其对支气管镜到达活检部位时间的影响因素。方法:回顾性分析2022年8月—2023年3月在宜昌市中心人民医院呼吸内镜中心接受径向超声引导下经支气管镜肺活检的116例肺外周结节患者,根据术前是否采用手绘导航规划路径,分为手绘导航联合径向超声组(手绘导航+RP-EBUS-GS组,n=60)和径向超声组(RP-EBUS-GS组,n=56),对比分析手绘导航在支气管镜肺活检中的应用效果。采用单因素及多因素Logistic回归分析手绘导航对支气管镜到达活检部位时间的影响因素。结果:手绘导航+RP-EBUS-GS组支气管镜到达活检部位时间明显短于RP-EBUS-GS组(6.32±3.10 min vs 8.89±4.09 min,P<0.001)。以支气管镜到达活检部位时间为因变量,单因素分析结果显示,两组患者性别、结节与支气管关系、所在支气管分级及是否应用手绘导航有明显差异(均P<0.05);多因素Logistic结果显示,所在支气管分级及是否应用手绘导航是支气管镜到达活检部位时间的独立影响因素(均P<0.05)。结论:手绘导航在肺外周结节患者行支气管镜肺活检中可明显缩短支气管镜到达活检部位的时间,具有很好的临床应用价值。肺外周结节所在支气管分级及是否应用手绘导航是支气管镜到达肺活检部位时间的独立影响因素。
