Introduction:Female breast nodules represent the most frequently detected lesions during breast ultrasound screening.Notably,nodules classified as BIRADS 4 or 5 indicate an elevated risk of breast cancer.Nevertheless,...Introduction:Female breast nodules represent the most frequently detected lesions during breast ultrasound screening.Notably,nodules classified as BIRADS 4 or 5 indicate an elevated risk of breast cancer.Nevertheless,the detection rate and BI-RADS classification of female breast nodules across China remain largely undocumented.Methods:This study analyzed health examination data from 6,412,893 urban women across 31 provincial-level administrative divisions(PLADs).We calculated detection rates of breast nodules and their various BI-RADS classifications.Chi-square(χ2)tests were performed to compare differences between groups.Multivariable logistic regression models were constructed to explore associations between breast nodules and BI-RADS 4-5 with demographic,socioeconomic,and metabolic indicators.Results:The overall detection rate of breast nodules in Chinese urban women was 27.9%,with provincial rates ranging from 11.6%to 37.0%.Among women with breast nodules marked with BI-RADS classification information,95.9%were categorized as BI-RADS 2-3,while 4.0%were classified as BI-RADS 4-5.Further analyses revealed that age,geographic region,per capita gross domestic product(GDP),body mass index(BMI),high triglyceride(TG),high lowdensity lipoprotein cholesterol(LDL-C),and diabetes were significant risk factors for BI-RADS 4-5 classification.Conclusions:This study highlights the importance of managing high-risk women with breast nodules through BI-RADS classification,underscoring the need for targeted health interventions while considering regional and socioeconomic disparities.展开更多
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
目的探讨超微血流成像(SMI)联合高级动态血流成像(ADF)鉴别最大径≤10 mm BI-RADS 4类乳腺结节良恶性的临床价值。方法选取我院经手术病理证实的78例女性乳腺结节患者(共81个病灶),其中良性结节47个,恶性结节34个,均行SMI和ADF获取病灶...目的探讨超微血流成像(SMI)联合高级动态血流成像(ADF)鉴别最大径≤10 mm BI-RADS 4类乳腺结节良恶性的临床价值。方法选取我院经手术病理证实的78例女性乳腺结节患者(共81个病灶),其中良性结节47个,恶性结节34个,均行SMI和ADF获取病灶血流分级和血管形态特征,比较良恶性结节上述检查结果的差异。分析SMI、ADF及两者联合应用鉴别BI-RADS 4类乳腺结节良恶性的诊断效能,采用Kappa检验分析其与病理结果的一致性。结果SMI检查显示乳腺良恶性结节血流分级和血管形态特征比较差异均有统计学意义(均P<0.001);ADF检查显示乳腺良恶性结节血流分级和血管形态特征比较差异均有统计学意义(均P<0.001)。SMI准确诊断BI-RADS 4类乳腺良性结节38个,恶性结节28个,诊断灵敏度、特异度、准确率分别为82.35%、80.85%、81.48%;ADF准确诊断BIRADS 4类乳腺良性结节32个,恶性结节25个,诊断灵敏度、特异度、准确率分别为73.53%、68.09%、70.37%;两者联合应用准确诊断BI-RADS 4类乳腺良性结节35个,恶性结节33个,诊断灵敏度、特异度、准确率分别为97.06%、74.47%、83.95%。SMI、ADF及两者联合应用与病理结果的一致性均中等(Kappa=0.632、0.406、0.685,均P<0.05)。结论SMI联合ADF可以提高最大径≤10 mm BI-RADS 4类乳腺结节良恶性的鉴别诊断效能,具有一定的临床价值。展开更多
超声因其便捷、无辐射等优势成为乳腺癌早期筛查的重要方式^([1]),根据美国放射学会发布的第5版乳腺影像报告与数据系统(breast imaging reporting and data system,BI-RADS),4类乳腺病变的恶性可能性为2%~95%,其跨度相对较大,且病变的...超声因其便捷、无辐射等优势成为乳腺癌早期筛查的重要方式^([1]),根据美国放射学会发布的第5版乳腺影像报告与数据系统(breast imaging reporting and data system,BI-RADS),4类乳腺病变的恶性可能性为2%~95%,其跨度相对较大,且病变的超声特征多样,易受诊断医师主观判断影响。在乳腺癌诊治指南中^([2]),BI-RADS 4类结节均建议行细胞学检查或病理活检,最终导致非必要穿刺活检及手术率较高。展开更多
Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interd...Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interdisciplinarity indicators are widely used to evaluate research performance,the impact of classification granularity on these assessments remains underexplored.Design/methodology/approach:This study investigates how different levels of classification granularity-macro,meso,and micro-affect the evaluation of interdisciplinarity in research institutes.Using a dataset of 262 institutes from four major German non-university organizations(FHG,HGF,MPG,WGL)from 2018 to 2022,we examine inconsistencies in interdisciplinarity across levels,analyze ranking changes,and explore the influence of institutional fields and research focus(applied vs.basic).Findings:Our findings reveal significant inconsistencies in interdisciplinarity across classification levels,with rankings varying substantially.Notably,the Fraunhofer Society(FHG),which performs well at the macro level,experiences significant ranking declines at meso and micro levels.Normalizing interdisciplinarity by research field confirmed that these declines persist.The research focus of institutes,whether applied,basic,or mixed,does not significantly explain the observed ranking dynamics.Research limitations:This study has only considered the publication-based dimension of institutional interdisciplinarity and has not explored other aspects.Practical implications:The findings provide insights for policymakers,research managers,and scholars to better interpret interdisciplinarity metrics and support interdisciplinary research effectively.Originality/value:This study underscores the critical role of classification granularity in interdisciplinarity assessment and emphasizes the need for standardized approaches to ensure robust and fair evaluations.展开更多
Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome...Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.展开更多
The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textile...The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.展开更多
Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronar...Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.展开更多
基金Strategic Research and Consultancy Project of the Chinese Academy of Engineering(2023-JB-11).
文摘Introduction:Female breast nodules represent the most frequently detected lesions during breast ultrasound screening.Notably,nodules classified as BIRADS 4 or 5 indicate an elevated risk of breast cancer.Nevertheless,the detection rate and BI-RADS classification of female breast nodules across China remain largely undocumented.Methods:This study analyzed health examination data from 6,412,893 urban women across 31 provincial-level administrative divisions(PLADs).We calculated detection rates of breast nodules and their various BI-RADS classifications.Chi-square(χ2)tests were performed to compare differences between groups.Multivariable logistic regression models were constructed to explore associations between breast nodules and BI-RADS 4-5 with demographic,socioeconomic,and metabolic indicators.Results:The overall detection rate of breast nodules in Chinese urban women was 27.9%,with provincial rates ranging from 11.6%to 37.0%.Among women with breast nodules marked with BI-RADS classification information,95.9%were categorized as BI-RADS 2-3,while 4.0%were classified as BI-RADS 4-5.Further analyses revealed that age,geographic region,per capita gross domestic product(GDP),body mass index(BMI),high triglyceride(TG),high lowdensity lipoprotein cholesterol(LDL-C),and diabetes were significant risk factors for BI-RADS 4-5 classification.Conclusions:This study highlights the importance of managing high-risk women with breast nodules through BI-RADS classification,underscoring the need for targeted health interventions while considering regional and socioeconomic disparities.
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.
文摘超声因其便捷、无辐射等优势成为乳腺癌早期筛查的重要方式^([1]),根据美国放射学会发布的第5版乳腺影像报告与数据系统(breast imaging reporting and data system,BI-RADS),4类乳腺病变的恶性可能性为2%~95%,其跨度相对较大,且病变的超声特征多样,易受诊断医师主观判断影响。在乳腺癌诊治指南中^([2]),BI-RADS 4类结节均建议行细胞学检查或病理活检,最终导致非必要穿刺活检及手术率较高。
文摘Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interdisciplinarity indicators are widely used to evaluate research performance,the impact of classification granularity on these assessments remains underexplored.Design/methodology/approach:This study investigates how different levels of classification granularity-macro,meso,and micro-affect the evaluation of interdisciplinarity in research institutes.Using a dataset of 262 institutes from four major German non-university organizations(FHG,HGF,MPG,WGL)from 2018 to 2022,we examine inconsistencies in interdisciplinarity across levels,analyze ranking changes,and explore the influence of institutional fields and research focus(applied vs.basic).Findings:Our findings reveal significant inconsistencies in interdisciplinarity across classification levels,with rankings varying substantially.Notably,the Fraunhofer Society(FHG),which performs well at the macro level,experiences significant ranking declines at meso and micro levels.Normalizing interdisciplinarity by research field confirmed that these declines persist.The research focus of institutes,whether applied,basic,or mixed,does not significantly explain the observed ranking dynamics.Research limitations:This study has only considered the publication-based dimension of institutional interdisciplinarity and has not explored other aspects.Practical implications:The findings provide insights for policymakers,research managers,and scholars to better interpret interdisciplinarity metrics and support interdisciplinary research effectively.Originality/value:This study underscores the critical role of classification granularity in interdisciplinarity assessment and emphasizes the need for standardized approaches to ensure robust and fair evaluations.
基金supported by King Saud University,Riyadh,Saudi Arabia,through the Researchers Supporting Project under Grant RSPD2025R697.
文摘Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.
基金supported in part by the Six Talent Peaks Project in Jiangsu Province under Grant 013040315in part by the China Textile Industry Federation Science and Technology Guidance Project under Grant 2017107+1 种基金in part by the National Natural Science Foundation of China under Grant 31570714in part by the China Scholarship Council under Grant 202108320290。
文摘The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.
基金the Research Grant of Kwangwoon University in 2024.
文摘Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.
