This study combines ground penetrating radar(GPR)and convolutional neural networks for the intelligent detection of underground road targets.The target location was realized using a gradient-class activation map(Grad-...This study combines ground penetrating radar(GPR)and convolutional neural networks for the intelligent detection of underground road targets.The target location was realized using a gradient-class activation map(Grad-CAM).First,GPR technology was used to detect roads and obtain radar images.This study constructs a radar image dataset containing 3000 underground road radar targets,such as underground pipelines and holes.Based on the dataset,a ResNet50 network was used to classify and train different underground targets.During training,the accuracy of the training set gradually increases and finally fluctuates approximately 85%.The loss function gradually decreases and falls between 0.2 and 0.3.Finally,targets were located using Grad-CAM.The positioning results of single and multiple targets are consistent with the actual position,indicating that the method can eff ectively realize the intelligent detection of underground targets in GPR.展开更多
鉴于Single Shot Multibox Detector(SSD)算法对中小目标检测时会出现漏检甚至错检的情况,提出一种改进的SSD目标检测算法,以提高中小目标检测的准确性.运用Gradient-weighted Class Activation Mapping(Grad-CAM)技术对检测过程中的细...鉴于Single Shot Multibox Detector(SSD)算法对中小目标检测时会出现漏检甚至错检的情况,提出一种改进的SSD目标检测算法,以提高中小目标检测的准确性.运用Gradient-weighted Class Activation Mapping(Grad-CAM)技术对检测过程中的细节作可视化处理,并以类激活图的形式呈现各检测层细节,分析各检测层的类激活图发现SSD算法中待检测目标的错检以及中小目标的漏检现象与回归损失函数相关.据此,采用Kullback-Leibler(KL)边框回归损失策略,利用Non Maximum Suppression(NMS)算法输出最终预测框.实验结果表明,改进算法相较于已有检测算法具有更高的准确率以及稳定性.展开更多
The cavitation in axial piston pumps threatens the reliability and safety of the overall hydraulic system.Vibration signal can reflect the cavitation conditions in axial piston pumps and it has been combined with mach...The cavitation in axial piston pumps threatens the reliability and safety of the overall hydraulic system.Vibration signal can reflect the cavitation conditions in axial piston pumps and it has been combined with machine learning to detect the pump cavitation.However,the vibration signal usually contains noise in real working conditions,which raises concerns about accurate recognition of cavitation in noisy environment.This paper presents an intelligent method to recognise the cavitation in axial piston pumps in noisy environment.First,we train a convolutional neural network(CNN)using the spectrogram images transformed from raw vibration data under different cavitation conditions.Second,we employ the technique of gradient-weighted class activation mapping(Grad-CAM)to visualise class-discriminative regions in the spectrogram image.Finally,we propose a novel image processing method based on Grad-CAM heatmap to automatically remove entrained noise and enhance class features in the spectrogram image.The experimental results show that the proposed method greatly improves the diagnostic performance of the CNN model in noisy environments.The classification accuracy of cavitation conditions increases from 0.50 to 0.89 and from 0.80 to 0.92 at signal-to-noise ratios of 4 and 6 dB,respectively.展开更多
Artificial intelligence(AI)[1,2]allows computers to think and behave like humans,so it is now becoming more and more influential in almost every field[3].Hence,users in businesses,industries,hospitals[4],etc.,need to ...Artificial intelligence(AI)[1,2]allows computers to think and behave like humans,so it is now becoming more and more influential in almost every field[3].Hence,users in businesses,industries,hospitals[4],etc.,need to understand how these AI models work[5]and the potential impact of using them.展开更多
Corona Virus(COVID-19)is a novel virus that crossed an animal-human barrier and emerged in Wuhan,China.Until now it has affected more than 119 million people.Detection of COVID-19 is a critical task and due to a large...Corona Virus(COVID-19)is a novel virus that crossed an animal-human barrier and emerged in Wuhan,China.Until now it has affected more than 119 million people.Detection of COVID-19 is a critical task and due to a large number of patients,a shortage of doctors has occurred for its detection.In this paper,a model has been suggested that not only detects the COVID-19 using X-ray and CT-Scan images but also shows the affected areas.Three classes have been defined;COVID-19,normal,and Pneumonia for X-ray images.For CT-Scan images,2 classes have been defined COVID-19 and non-COVID-19.For classi-fication purposes,pretrained models like ResNet50,VGG-16,and VGG19 have been used with some tuning.For detecting the affected areas Gradient-weighted Class Activation Mapping(GradCam)has been used.As the X-rays and ct images are taken at different intensities,so the contrast limited adaptive histogram equalization(CLAHE)has been applied to see the effect on the training of the models.As a result of these experiments,we achieved a maximum validation accuracy of 88.10%with a training accuracy of 88.48%for CT-Scan images using the ResNet50 model.While for X-ray images we achieved a maximum validation accuracy of 97.31%with a training accuracy of 95.64%using the VGG16 model.展开更多
Remote sensing image scene classification and remote sensing technology applications are hot research topics.Although CNN-based models have reached high average accuracy,some classes are still misclassified,such as“f...Remote sensing image scene classification and remote sensing technology applications are hot research topics.Although CNN-based models have reached high average accuracy,some classes are still misclassified,such as“freeway,”“spare residential,”and“commercial_area.”These classes contain typical decisive features,spatial-relation features,and mixed decisive and spatial-relation features,which limit high-quality image scene classification.To address this issue,this paper proposes a Grad-CAM and capsule network hybrid method for image scene classification.The Grad-CAM and capsule network structures have the potential to recognize decisive features and spatial-relation features,respectively.By using a pre-trained model,hybrid structure,and structure adjustment,the proposed model can recognize both decisive and spatial-relation features.A group of experiments is designed on three popular data sets with increasing classification difficulties.In the most advanced experiment,92.67%average accuracy is achieved.Specifically,83%,75%,and 86%accuracies are obtained in the classes of“church,”“palace,”and“commercial_area,”respectively.This research demonstrates that the hybrid structure can effectively improve performance by considering both decisive and spatial-relation features.Therefore,Grad-CAM-CapsNet is a promising and powerful structure for image scene classification.展开更多
乳腺癌持续位居全球女性癌症发病与致死的主要原因之列。早期且精确的诊断对于优化患者预后具有举足轻重的地位。乳房X线摄影、超声检查及磁共振成像(Magnetic Resonance Imaging, MRI)等影像学技术在乳腺癌的诊断中扮演着至关重要的角...乳腺癌持续位居全球女性癌症发病与致死的主要原因之列。早期且精确的诊断对于优化患者预后具有举足轻重的地位。乳房X线摄影、超声检查及磁共振成像(Magnetic Resonance Imaging, MRI)等影像学技术在乳腺癌的诊断中扮演着至关重要的角色。然而,这些技术手段面临着准确性波动、操作员依赖性显著及结果阐释困难等多重挑战。在此背景下,人工智能(Artificial Intelligence, AI),尤其是可解释人工智能(Explainable Artificial Intelligence, XAI)的融入,已成为提升诊断精确度及增强信任度的革命性途径。本综述聚焦于XAI技术在乳腺癌诊断领域内,于不同成像模式中的应用效果比较。深入探讨了核心的XAI方法,诸如Shapley加性解释(SHAP)、局部可解释模型无关解释(LIME)以及基于梯度的类激活映射(Grad-CAM),着重阐述了它们在增进模型可解释性及提升临床实用性方面的具体成效。综述不仅分析了XAI技术在乳房X线摄影、超声及MRI应用中的优势与局限,还特别强调了其在提高AI辅助预测透明度方面的贡献。此外,本文亦评估了XAI在应对假阳性、假阴性问题以及多模态成像数据整合挑战中的效能。该评论的核心价值在于,它全面剖析了XAI在缩小AI技术进展与临床实际应用之间鸿沟的潜力。通过提升透明度,XAI技术能够增强临床医生对AI的信任度,促进其更顺畅地融入诊断工作流程,从而助力个性化医疗实践的推进及患者治疗成效的改善。综上所述,尽管XAI在提升AI模型可解释性与准确性方面取得了显著进展,但在计算复杂度控制、普遍适用性拓展及临床接纳度提升等方面仍面临诸多挑战。未来研究应着重于优化XAI方法、促进跨学科间的深度合作,并开发标准化的框架体系,以确保XAI技术能在多样化的临床环境中实现可扩展性与可靠性的双重提升。Breast cancer remains one of the leading causes of cancer incidence and mortality among women worldwide. Early and accurate diagnosis plays a pivotal role in optimizing patient prognosis. Imaging techniques such as mammography, ultrasound, and magnetic resonance imaging (MRI) play crucial roles in the diagnosis of breast cancer. However, these techniques face multiple challenges, including accuracy fluctuations, significant operator dependency, and difficulties in result interpretation. In this context, the integration of Artificial Intelligence (AI), especially Explainable Artificial Intelligence (XAI), has become a revolutionary approach to improving diagnostic accuracy and enhancing trust. This review focuses on the comparative application of XAI technologies across different imaging modalities in breast cancer diagnosis. It delves into core XAI methods such as Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Gradient-weighted Class Activation Mapping (Grad-CAM), with an emphasis on their effectiveness in enhancing model interpretability and improving clinical utility. The review analyzes not only the advantages and limitations of XAI in mammography, ultrasound, and MRI applications but also highlights its contribution to increasing the transparency of AI-assisted predictions. Additionally, the review evaluates the performance of XAI in addressing issues related to false positives, false negatives, and the challenges of multimodal imaging data integration. The core value of this review lies in its comprehensive analysis of the potential of XAI in bridging the gap between advancements in AI technology and clinical application. By enhancing transparency, XAI can boost clinicians’ trust in AI, facilitating its smoother integration into diagnostic workflows, thereby promoting personalized medical practices and improving patient treatment outcomes. In conclusion, despite significant progress made by XAI in improving AI model interpretability and accuracy, challenges remain in terms of computational complexity, general applicability, and clinical acceptance. Future research should focus on optimizing XAI methods, fostering interdisciplinary collaboration, and developing standardized frameworks to ensure the scalability and reliability of XAI technologies in diverse clinical environments.展开更多
基金supported in part by the National Natural Science Fund of China under Grant 52074306in part by the National Key Research and Development Program of China under Grant 2019YFC1805504in part by the Fundamental Research Funds for the Central Universities under Grant 2023JCCXHH02。
文摘This study combines ground penetrating radar(GPR)and convolutional neural networks for the intelligent detection of underground road targets.The target location was realized using a gradient-class activation map(Grad-CAM).First,GPR technology was used to detect roads and obtain radar images.This study constructs a radar image dataset containing 3000 underground road radar targets,such as underground pipelines and holes.Based on the dataset,a ResNet50 network was used to classify and train different underground targets.During training,the accuracy of the training set gradually increases and finally fluctuates approximately 85%.The loss function gradually decreases and falls between 0.2 and 0.3.Finally,targets were located using Grad-CAM.The positioning results of single and multiple targets are consistent with the actual position,indicating that the method can eff ectively realize the intelligent detection of underground targets in GPR.
文摘鉴于Single Shot Multibox Detector(SSD)算法对中小目标检测时会出现漏检甚至错检的情况,提出一种改进的SSD目标检测算法,以提高中小目标检测的准确性.运用Gradient-weighted Class Activation Mapping(Grad-CAM)技术对检测过程中的细节作可视化处理,并以类激活图的形式呈现各检测层细节,分析各检测层的类激活图发现SSD算法中待检测目标的错检以及中小目标的漏检现象与回归损失函数相关.据此,采用Kullback-Leibler(KL)边框回归损失策略,利用Non Maximum Suppression(NMS)算法输出最终预测框.实验结果表明,改进算法相较于已有检测算法具有更高的准确率以及稳定性.
基金National Key R&D Program of China,Grant/Award Number:2018YFB1702503Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems,Grant/Award Number:GZKF-202108+2 种基金Open Foundation of the Guangdong Provincial Key Laboratory of Electronic Information Products Reliability TechnologyChina National Postdoctoral Program for Innovative Talents,Grant/Award Number:BX20200210China Postdoctoral Science Foundation,Grant/Award Number:2019M660086。
文摘The cavitation in axial piston pumps threatens the reliability and safety of the overall hydraulic system.Vibration signal can reflect the cavitation conditions in axial piston pumps and it has been combined with machine learning to detect the pump cavitation.However,the vibration signal usually contains noise in real working conditions,which raises concerns about accurate recognition of cavitation in noisy environment.This paper presents an intelligent method to recognise the cavitation in axial piston pumps in noisy environment.First,we train a convolutional neural network(CNN)using the spectrogram images transformed from raw vibration data under different cavitation conditions.Second,we employ the technique of gradient-weighted class activation mapping(Grad-CAM)to visualise class-discriminative regions in the spectrogram image.Finally,we propose a novel image processing method based on Grad-CAM heatmap to automatically remove entrained noise and enhance class features in the spectrogram image.The experimental results show that the proposed method greatly improves the diagnostic performance of the CNN model in noisy environments.The classification accuracy of cavitation conditions increases from 0.50 to 0.89 and from 0.80 to 0.92 at signal-to-noise ratios of 4 and 6 dB,respectively.
基金Sino-UK Education Fund(OP202006)Royal Society(RP202G0230)+8 种基金MRC(MC_PC_17171)BHF(AA/18/3/34220)Hope Foundation for Cancer Research(RM60G0680)GCRF(P202PF11)BBSRC(RM32G0178B8)Sino-UK Industrial Fund(RP202G0289)Data Science Enhancement Fund(P202RE237)LIAS(P202ED10&P202RE969)Fight for Sight(24NN201).
文摘Artificial intelligence(AI)[1,2]allows computers to think and behave like humans,so it is now becoming more and more influential in almost every field[3].Hence,users in businesses,industries,hospitals[4],etc.,need to understand how these AI models work[5]and the potential impact of using them.
文摘Corona Virus(COVID-19)is a novel virus that crossed an animal-human barrier and emerged in Wuhan,China.Until now it has affected more than 119 million people.Detection of COVID-19 is a critical task and due to a large number of patients,a shortage of doctors has occurred for its detection.In this paper,a model has been suggested that not only detects the COVID-19 using X-ray and CT-Scan images but also shows the affected areas.Three classes have been defined;COVID-19,normal,and Pneumonia for X-ray images.For CT-Scan images,2 classes have been defined COVID-19 and non-COVID-19.For classi-fication purposes,pretrained models like ResNet50,VGG-16,and VGG19 have been used with some tuning.For detecting the affected areas Gradient-weighted Class Activation Mapping(GradCam)has been used.As the X-rays and ct images are taken at different intensities,so the contrast limited adaptive histogram equalization(CLAHE)has been applied to see the effect on the training of the models.As a result of these experiments,we achieved a maximum validation accuracy of 88.10%with a training accuracy of 88.48%for CT-Scan images using the ResNet50 model.While for X-ray images we achieved a maximum validation accuracy of 97.31%with a training accuracy of 95.64%using the VGG16 model.
基金funded by the open fund of the Key Laboratory of Jianghuai Arable Land Resources Protection and Eco-restoration(Ministry of Natural Resources)(No.2022-ARPE-KF04)the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation(Ministry of Natural Resources)(No.KF-2020-05-084).
文摘Remote sensing image scene classification and remote sensing technology applications are hot research topics.Although CNN-based models have reached high average accuracy,some classes are still misclassified,such as“freeway,”“spare residential,”and“commercial_area.”These classes contain typical decisive features,spatial-relation features,and mixed decisive and spatial-relation features,which limit high-quality image scene classification.To address this issue,this paper proposes a Grad-CAM and capsule network hybrid method for image scene classification.The Grad-CAM and capsule network structures have the potential to recognize decisive features and spatial-relation features,respectively.By using a pre-trained model,hybrid structure,and structure adjustment,the proposed model can recognize both decisive and spatial-relation features.A group of experiments is designed on three popular data sets with increasing classification difficulties.In the most advanced experiment,92.67%average accuracy is achieved.Specifically,83%,75%,and 86%accuracies are obtained in the classes of“church,”“palace,”and“commercial_area,”respectively.This research demonstrates that the hybrid structure can effectively improve performance by considering both decisive and spatial-relation features.Therefore,Grad-CAM-CapsNet is a promising and powerful structure for image scene classification.
文摘乳腺癌持续位居全球女性癌症发病与致死的主要原因之列。早期且精确的诊断对于优化患者预后具有举足轻重的地位。乳房X线摄影、超声检查及磁共振成像(Magnetic Resonance Imaging, MRI)等影像学技术在乳腺癌的诊断中扮演着至关重要的角色。然而,这些技术手段面临着准确性波动、操作员依赖性显著及结果阐释困难等多重挑战。在此背景下,人工智能(Artificial Intelligence, AI),尤其是可解释人工智能(Explainable Artificial Intelligence, XAI)的融入,已成为提升诊断精确度及增强信任度的革命性途径。本综述聚焦于XAI技术在乳腺癌诊断领域内,于不同成像模式中的应用效果比较。深入探讨了核心的XAI方法,诸如Shapley加性解释(SHAP)、局部可解释模型无关解释(LIME)以及基于梯度的类激活映射(Grad-CAM),着重阐述了它们在增进模型可解释性及提升临床实用性方面的具体成效。综述不仅分析了XAI技术在乳房X线摄影、超声及MRI应用中的优势与局限,还特别强调了其在提高AI辅助预测透明度方面的贡献。此外,本文亦评估了XAI在应对假阳性、假阴性问题以及多模态成像数据整合挑战中的效能。该评论的核心价值在于,它全面剖析了XAI在缩小AI技术进展与临床实际应用之间鸿沟的潜力。通过提升透明度,XAI技术能够增强临床医生对AI的信任度,促进其更顺畅地融入诊断工作流程,从而助力个性化医疗实践的推进及患者治疗成效的改善。综上所述,尽管XAI在提升AI模型可解释性与准确性方面取得了显著进展,但在计算复杂度控制、普遍适用性拓展及临床接纳度提升等方面仍面临诸多挑战。未来研究应着重于优化XAI方法、促进跨学科间的深度合作,并开发标准化的框架体系,以确保XAI技术能在多样化的临床环境中实现可扩展性与可靠性的双重提升。Breast cancer remains one of the leading causes of cancer incidence and mortality among women worldwide. Early and accurate diagnosis plays a pivotal role in optimizing patient prognosis. Imaging techniques such as mammography, ultrasound, and magnetic resonance imaging (MRI) play crucial roles in the diagnosis of breast cancer. However, these techniques face multiple challenges, including accuracy fluctuations, significant operator dependency, and difficulties in result interpretation. In this context, the integration of Artificial Intelligence (AI), especially Explainable Artificial Intelligence (XAI), has become a revolutionary approach to improving diagnostic accuracy and enhancing trust. This review focuses on the comparative application of XAI technologies across different imaging modalities in breast cancer diagnosis. It delves into core XAI methods such as Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Gradient-weighted Class Activation Mapping (Grad-CAM), with an emphasis on their effectiveness in enhancing model interpretability and improving clinical utility. The review analyzes not only the advantages and limitations of XAI in mammography, ultrasound, and MRI applications but also highlights its contribution to increasing the transparency of AI-assisted predictions. Additionally, the review evaluates the performance of XAI in addressing issues related to false positives, false negatives, and the challenges of multimodal imaging data integration. The core value of this review lies in its comprehensive analysis of the potential of XAI in bridging the gap between advancements in AI technology and clinical application. By enhancing transparency, XAI can boost clinicians’ trust in AI, facilitating its smoother integration into diagnostic workflows, thereby promoting personalized medical practices and improving patient treatment outcomes. In conclusion, despite significant progress made by XAI in improving AI model interpretability and accuracy, challenges remain in terms of computational complexity, general applicability, and clinical acceptance. Future research should focus on optimizing XAI methods, fostering interdisciplinary collaboration, and developing standardized frameworks to ensure the scalability and reliability of XAI technologies in diverse clinical environments.
