Purpose-Current multi-source image fusion methods frequently overlook the issue of detailed features when employing deep learning technology,resulting in inadequate target feature information.In real-world mission sce...Purpose-Current multi-source image fusion methods frequently overlook the issue of detailed features when employing deep learning technology,resulting in inadequate target feature information.In real-world mission scenarios,such as military information acquisition or medical image enhancement,the prominence of target feature information is of paramount importance.To address these challenges,this paper introduces a novel infrared-visible light fusion model.Design/methodology/approach-Leveraging the foundational architecture of the traditional DenseFuse model,this paper optimizes the backbone network structure and incorporates a Unique Feature Encoder(UFE)to meticulously extract the distinctive features inherent in the two images.Furthermore,it integrates the Convolutional Block Attention Module(CBAM)and the Squeeze and Excitation Network(SE)to enhance and replace the original spatial and channel attention mechanisms.Findings-Compared to other methods such as IFCNN,NestFuse,DenseFuse,etc.,the values of entropy,standard deviation,and mutual information index of the method presented in this paper can reach 6.9985,82.6652,and 13.6022,respectively,which are significantly improved compared with other methods.Originality/value-This paper presents a UFEFusion framework that synergizes with the CBAM attention mechanism to markedly augment the extraction of detailed features relative to other methods.Moreover,the framework adeptly extracts and amplifies unique features from disparate images,thereby elevating the overall feature representation capability.展开更多
精神康复患者及家属专家(User and family Expert,简称UFE)同伴支持,建立在具有相同经历的精神障碍患者互相提供情感、信息支持和希望的基础上,是一个给予帮助和接受帮助的患者康复支持系统。相关研究和实践表明,精神康复UFE同伴支持服...精神康复患者及家属专家(User and family Expert,简称UFE)同伴支持,建立在具有相同经历的精神障碍患者互相提供情感、信息支持和希望的基础上,是一个给予帮助和接受帮助的患者康复支持系统。相关研究和实践表明,精神康复UFE同伴支持服务糢式对接受服务的患者,提供服务的“UFE”以及精神卫生系统均有重要意义。文章在梳理精神康复UFE同伴支持发展昧络及相关研究基础上,通过分析北京大学第六医院绿丝带志愿者协会的同伴支持服务,对同伴支持本土化进行探讨,以期为我国同伴支持服务发展提供借鉴。展开更多
基金funded by Basic Research Project of the National Defence Science and Industry Bureau(Project No.JCKY2022405C010)the Basic Research Project of the Translational Application Project of the“Wise Eyes Action”(Project No.F2B6A194)Beijing Information Science and Technology University Education Reform(Project No.2024JGYB35).
文摘Purpose-Current multi-source image fusion methods frequently overlook the issue of detailed features when employing deep learning technology,resulting in inadequate target feature information.In real-world mission scenarios,such as military information acquisition or medical image enhancement,the prominence of target feature information is of paramount importance.To address these challenges,this paper introduces a novel infrared-visible light fusion model.Design/methodology/approach-Leveraging the foundational architecture of the traditional DenseFuse model,this paper optimizes the backbone network structure and incorporates a Unique Feature Encoder(UFE)to meticulously extract the distinctive features inherent in the two images.Furthermore,it integrates the Convolutional Block Attention Module(CBAM)and the Squeeze and Excitation Network(SE)to enhance and replace the original spatial and channel attention mechanisms.Findings-Compared to other methods such as IFCNN,NestFuse,DenseFuse,etc.,the values of entropy,standard deviation,and mutual information index of the method presented in this paper can reach 6.9985,82.6652,and 13.6022,respectively,which are significantly improved compared with other methods.Originality/value-This paper presents a UFEFusion framework that synergizes with the CBAM attention mechanism to markedly augment the extraction of detailed features relative to other methods.Moreover,the framework adeptly extracts and amplifies unique features from disparate images,thereby elevating the overall feature representation capability.
文摘精神康复患者及家属专家(User and family Expert,简称UFE)同伴支持,建立在具有相同经历的精神障碍患者互相提供情感、信息支持和希望的基础上,是一个给予帮助和接受帮助的患者康复支持系统。相关研究和实践表明,精神康复UFE同伴支持服务糢式对接受服务的患者,提供服务的“UFE”以及精神卫生系统均有重要意义。文章在梳理精神康复UFE同伴支持发展昧络及相关研究基础上,通过分析北京大学第六医院绿丝带志愿者协会的同伴支持服务,对同伴支持本土化进行探讨,以期为我国同伴支持服务发展提供借鉴。
文摘目的:研究Sysmex UF5000尿沉渣分析仪(UF5000)检测尿液中的白细胞(white blood cell,WBC)计数、细菌(bacterium,BACT)计数用于筛查婴幼儿细菌性尿路感染的诊断价值。方法:回顾性分析497例疑似尿路感染婴幼儿的中段尿培养结果和UF5000检测结果并进行统计。根据菌落生长数量将标本分为3组(<1×10^(4) CFU/mL,10^(4)~10^(5)CFU/mL组和>1×10^(5)CFU/mL组),比较各组之间WBC、BACT水平的差异;绘制ROC曲线,计算WBC、BACT用于筛查婴幼儿细菌性尿路感染的cut-off值。结果:497例尿液标本中阳性率9.46%,其中革兰阴性菌(G-)80.85%,主要为大肠埃希氏菌(59.57%)。三组标本间水平比较:BACT水平差异均有统计学意义(P值均<0.001),且水平随着<1×10^(4)CFU/mL,10^(4)~10^(5)CFU/mL组和>1×10^(5)CFU/mL组顺序逐渐升高;WBC水平组间比较显示表示<1×10^(4)CFU/mL和10^(4)~10^(5)CFU/mL组的WBC水平并无统计学差异(P=0.161),其余各组间比较有差异。ROC曲线分析显示WBC筛查尿路感染的男、女性曲线下面积,(Area under the curve,AUC)分别为:0.843和0.910,BACT筛查尿路感染的男、女性曲线下面积分别为:0.951和0.908。结论:本院婴幼儿细菌性尿路感染以大肠埃希氏菌为主。当WBC<34.35/μL(男)或<13.75/μL(女),BACT<25.30/μL(男)或<89.05/μL(女)时,有助于临床快速有效地排除尿路感染。
