Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services...Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.展开更多
In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This...In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This method combines two core modules:a simple parameter-free self-attention and cross-attention.By analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot conditions.The proposed cross-correlation network integrates these two modules and is trained in an end-to-end manner.The simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between images.Compared with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target objects.Experimental results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.展开更多
随着超声和钼靶技术的应用,越来越多的乳腺病灶被检出,如何正确判断其检查结果已成为研究的热点[1]。乳腺影像报告和数据系统（breast imaging reporting and data system,BI-RADS）对规范乳腺影像报告、减少描述混淆以及对临床诊治均...随着超声和钼靶技术的应用,越来越多的乳腺病灶被检出,如何正确判断其检查结果已成为研究的热点[1]。乳腺影像报告和数据系统（breast imaging reporting and data system,BI-RADS）对规范乳腺影像报告、减少描述混淆以及对临床诊治均起着很大的作用[2]。本研究回顾性分析甘肃省肿瘤医院超声和钼靶报告均完整的200例乳腺恶性肿瘤及63例良性病变临床资料，评价BI-RADS分级在乳腺癌诊断中的价值。展开更多
目的:探讨新版(2013年更新版)超声(ultrasound,US)乳腺影像报告数据系统(breast imaging report and data system,BI-RADS)中罗列的指标结合相关临床资料在诊断乳腺癌中的应用情况,评估新版超声BI-RADS(BI-RADS-US)在乳腺癌诊断中的临...目的:探讨新版(2013年更新版)超声(ultrasound,US)乳腺影像报告数据系统(breast imaging report and data system,BI-RADS)中罗列的指标结合相关临床资料在诊断乳腺癌中的应用情况,评估新版超声BI-RADS(BI-RADS-US)在乳腺癌诊断中的临床应用价值。方法:3名超声医师结合收集的临床资料共同对2 860个肿块的声像图进行回顾性分析,按照新版BIRADS超声影像学词典记录、分类。以病理结果为金标准,运用ROC曲线计算新版BI-RADS-US分类的诊断效能。并对记录的超声指标及收集的临床资料先行单因素分析,具有统计学意义的指标再运用多因素logistic回归分析进行分析。结果:新版BIRADS-US诊断乳腺癌BI-RADS 2类的恶性率为0.66%,3类的恶性率0.99%,4a类的恶性率为9.57%,4b类的恶性率为32.31%,4c类的恶性率为88.36%,5类的恶性率为94.19%。以4a类为截断点,新版BI-RADS-US诊断乳腺癌的敏感性为88.55%,特异性为92.17%,准确性为91.75%,AUC为0.948,Youden指数为0.81。结论:新版BI-RADS-US诊断乳腺癌风险分层的准确率高。以4a类作为截断点,新版BI-RADS-US诊断乳腺癌具有较高的诊断效能。肿块形态、边缘、钙化、血流是重要的超声变量,结合患者年龄和腋窝淋巴结转移情况可指导临床进行明确的诊断和精确的治疗。展开更多
目的·探讨2013版超声乳腺影像报告和数据系统(breast imaging reporting and data system,BI-RADS)分类诊断标准结合剪切波弹性成像技术(shear wave elastography,SWE)鉴别乳腺良恶性病灶的价值。方法·对155例患者共175个乳...目的·探讨2013版超声乳腺影像报告和数据系统(breast imaging reporting and data system,BI-RADS)分类诊断标准结合剪切波弹性成像技术(shear wave elastography,SWE)鉴别乳腺良恶性病灶的价值。方法·对155例患者共175个乳腺病灶行常规超声检查,并用BI-RADS分类诊断标准判断其良恶性;再行剪切波弹性成像检测,获得乳腺良恶性病灶的剪切波定量参数。以病理结果为金标准,构建受试者操作特征(ROC)曲线,比较2种方法单独应用及联合应用的诊断价值。结果·BI-RADS分类诊断标准、SWE技术及两者联合鉴别诊断乳腺良恶性结节的曲线下面积(AUC)分别为0.913、0.884和0.957,三者两两比较,2种方法单独使用与两者联合应用的AUC差异皆有统计学意义(BI-RADS分类vs两者联合:Z=2.883,P=0.002;SWE技术vs两者联合:Z=4.081,P=0.000)。结论·BI-RADS分类与SWE技术联合可以提高乳腺病灶的诊断准确性。展开更多
目的:通过分析健康女性乳腺BI-RADS分类与其可能影响因素的关系,以更早期通过干预达到一级预防。方法:以我院近5年7 204例健康体检女性作为研究对象,分析其乳腺超声与年龄、BMI、甘油三酯、总胆固醇、高密度胆固醇(HDL-C)、葡萄糖、子...目的:通过分析健康女性乳腺BI-RADS分类与其可能影响因素的关系,以更早期通过干预达到一级预防。方法:以我院近5年7 204例健康体检女性作为研究对象,分析其乳腺超声与年龄、BMI、甘油三酯、总胆固醇、高密度胆固醇(HDL-C)、葡萄糖、子宫状态的关联,乳腺报告采用美国放射学会乳腺影像学报告和数据系统(Breast Imaging Reporting and Data System,BI-RADS)判读结果,对各项数据进行卡方检验与Logistic多因素分析。结果:年龄、甘油三酯、总胆固醇、HDL-C、葡萄糖对乳腺BI-RADS分类没有影响(P>0.05);BMI(P=0.004)与子宫状态(P=0.000)是BI-RADS分类的独立影响因素;低体质(OR=0.696,95%CI=0.502~0.966)、超重(OR=0.217,95%CI=0.142~0.333)、肥胖(OR=0.123,95%CI=0.066~0.231)与BI-RADS分类呈负相关,子宫缺失(OR=19.189,95%CI=14.055~26.198)、子宫肌瘤(OR=4.384,95%CI=3.499~5.492)、绝经期子宫(OR=3.283,95%CI=2.374~4.541)是BI-RADS高分类的危险因素。结论:BMI与子宫状态是BI-RADS高分类的独立危险因素,这种关联不依赖于其他因素独立存在。子宫状态与乳腺BI-RADS关联,发现子宫缺失对乳腺BI-RADS分类极具影响。展开更多
文摘Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.
基金Supported by the National Pre-research Program during the 14th Five-Year Plan(514010405)。
文摘In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This method combines two core modules:a simple parameter-free self-attention and cross-attention.By analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot conditions.The proposed cross-correlation network integrates these two modules and is trained in an end-to-end manner.The simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between images.Compared with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target objects.Experimental results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.
文摘随着超声和钼靶技术的应用,越来越多的乳腺病灶被检出,如何正确判断其检查结果已成为研究的热点[1]。乳腺影像报告和数据系统（breast imaging reporting and data system,BI-RADS）对规范乳腺影像报告、减少描述混淆以及对临床诊治均起着很大的作用[2]。本研究回顾性分析甘肃省肿瘤医院超声和钼靶报告均完整的200例乳腺恶性肿瘤及63例良性病变临床资料，评价BI-RADS分级在乳腺癌诊断中的价值。
文摘目的:探讨新版(2013年更新版)超声(ultrasound,US)乳腺影像报告数据系统(breast imaging report and data system,BI-RADS)中罗列的指标结合相关临床资料在诊断乳腺癌中的应用情况,评估新版超声BI-RADS(BI-RADS-US)在乳腺癌诊断中的临床应用价值。方法:3名超声医师结合收集的临床资料共同对2 860个肿块的声像图进行回顾性分析,按照新版BIRADS超声影像学词典记录、分类。以病理结果为金标准,运用ROC曲线计算新版BI-RADS-US分类的诊断效能。并对记录的超声指标及收集的临床资料先行单因素分析,具有统计学意义的指标再运用多因素logistic回归分析进行分析。结果:新版BIRADS-US诊断乳腺癌BI-RADS 2类的恶性率为0.66%,3类的恶性率0.99%,4a类的恶性率为9.57%,4b类的恶性率为32.31%,4c类的恶性率为88.36%,5类的恶性率为94.19%。以4a类为截断点,新版BI-RADS-US诊断乳腺癌的敏感性为88.55%,特异性为92.17%,准确性为91.75%,AUC为0.948,Youden指数为0.81。结论:新版BI-RADS-US诊断乳腺癌风险分层的准确率高。以4a类作为截断点,新版BI-RADS-US诊断乳腺癌具有较高的诊断效能。肿块形态、边缘、钙化、血流是重要的超声变量,结合患者年龄和腋窝淋巴结转移情况可指导临床进行明确的诊断和精确的治疗。
文摘目的·探讨2013版超声乳腺影像报告和数据系统(breast imaging reporting and data system,BI-RADS)分类诊断标准结合剪切波弹性成像技术(shear wave elastography,SWE)鉴别乳腺良恶性病灶的价值。方法·对155例患者共175个乳腺病灶行常规超声检查,并用BI-RADS分类诊断标准判断其良恶性;再行剪切波弹性成像检测,获得乳腺良恶性病灶的剪切波定量参数。以病理结果为金标准,构建受试者操作特征(ROC)曲线,比较2种方法单独应用及联合应用的诊断价值。结果·BI-RADS分类诊断标准、SWE技术及两者联合鉴别诊断乳腺良恶性结节的曲线下面积(AUC)分别为0.913、0.884和0.957,三者两两比较,2种方法单独使用与两者联合应用的AUC差异皆有统计学意义(BI-RADS分类vs两者联合:Z=2.883,P=0.002;SWE技术vs两者联合:Z=4.081,P=0.000)。结论·BI-RADS分类与SWE技术联合可以提高乳腺病灶的诊断准确性。
文摘目的:通过分析健康女性乳腺BI-RADS分类与其可能影响因素的关系,以更早期通过干预达到一级预防。方法:以我院近5年7 204例健康体检女性作为研究对象,分析其乳腺超声与年龄、BMI、甘油三酯、总胆固醇、高密度胆固醇(HDL-C)、葡萄糖、子宫状态的关联,乳腺报告采用美国放射学会乳腺影像学报告和数据系统(Breast Imaging Reporting and Data System,BI-RADS)判读结果,对各项数据进行卡方检验与Logistic多因素分析。结果:年龄、甘油三酯、总胆固醇、HDL-C、葡萄糖对乳腺BI-RADS分类没有影响(P>0.05);BMI(P=0.004)与子宫状态(P=0.000)是BI-RADS分类的独立影响因素;低体质(OR=0.696,95%CI=0.502~0.966)、超重(OR=0.217,95%CI=0.142~0.333)、肥胖(OR=0.123,95%CI=0.066~0.231)与BI-RADS分类呈负相关,子宫缺失(OR=19.189,95%CI=14.055~26.198)、子宫肌瘤(OR=4.384,95%CI=3.499~5.492)、绝经期子宫(OR=3.283,95%CI=2.374~4.541)是BI-RADS高分类的危险因素。结论:BMI与子宫状态是BI-RADS高分类的独立危险因素,这种关联不依赖于其他因素独立存在。子宫状态与乳腺BI-RADS关联,发现子宫缺失对乳腺BI-RADS分类极具影响。
