Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status...Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.展开更多
BACKGROUND Congestive hepatopathy,also known as nutmeg liver,is liver damage secondary to chronic heart failure(HF).Its morphological characteristics in terms of medical imaging are not defined and remain unclear.AIM ...BACKGROUND Congestive hepatopathy,also known as nutmeg liver,is liver damage secondary to chronic heart failure(HF).Its morphological characteristics in terms of medical imaging are not defined and remain unclear.AIM To leverage machine learning to capture imaging features of congestive hepatopathy using incidentally acquired computed tomography(CT)scans.METHODS We retrospectively analyzed 179 chronic HF patients who underwent echocardiography and CT within one year.Right HF severity was classified into three grades.Liver CT images at the paraumbilical vein level were used to develop a ResNet-based machine learning model to predict tricuspid regurgitation(TR)severity.Model accuracy was compared with that of six gastroenterology and four radiology experts.RESULTS In the included patients,120 were male(mean age:73.1±14.4 years).The accuracy of the results predicting TR severity from a single CT image for the machine learning model was significantly higher than the average accuracy of the experts.The model was found to be exceptionally reliable for predicting severe TR.CONCLUSION Deep learning models,particularly those using ResNet architectures,can help identify morphological changes associated with TR severity,aiding in early liver dysfunction detection in patients with HF,thereby improving outcomes.展开更多
Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have m...Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have made early contributions;however,recent advancements in deep learning(DL)have revolutionized the field,offering state-of-the-art performance in image classification,segmentation,detection,fusion,registration,and enhancement.This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks,highlighting both foundational models and recent innovations.The article begins by introducing conventional techniques and their limitations,setting the stage for DL-based solutions.Core DL architectures,including Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),Generative Adversarial Networks(GANs),Vision Transformers(ViTs),and hybrid models,are discussed in detail,including their advantages and domain-specific adaptations.Advanced learning paradigms such as semi-supervised learning,selfsupervised learning,and few-shot learning are explored for their potential to mitigate data annotation challenges in clinical datasets.This review further categorizes major tasks in medical image analysis,elaborating on how DL techniques have enabled precise tumor segmentation,lesion detection,modality fusion,super-resolution,and robust classification across diverse clinical settings.Emphasis is placed on applications in oncology,cardiology,neurology,and infectious diseases,including COVID-19.Challenges such as data scarcity,label imbalance,model generalizability,interpretability,and integration into clinical workflows are critically examined.Ethical considerations,explainable AI(XAI),federated learning,and regulatory compliance are discussed as essential components of real-world deployment.Benchmark datasets,evaluation metrics,and comparative performance analyses are presented to support future research.The article concludes with a forward-looking perspective on the role of foundation models,multimodal learning,edge AI,and bio-inspired computing in the future of medical imaging.Overall,this review serves as a valuable resource for researchers,clinicians,and developers aiming to harness deep learning for intelligent,efficient,and clinically viable medical image analysis.展开更多
Deep learning models have become a core technological tool in the field of medical image analysis.However,these models often suffer from a lack of transparency in their decision-making processes,leading to challenges ...Deep learning models have become a core technological tool in the field of medical image analysis.However,these models often suffer from a lack of transparency in their decision-making processes,leading to challenges related to trust and interpret ability in clinical applications.To address this issue,explainable artificial intelligence(XAI)techniques have been applied to medical image analysis.While showing promising potential,XAI also brings significant ethical risks in practice—most notably,the problem of spurious explanations.Such explanations may rise further concerns regarding patient privacy,data security,and the attribution of decisionmaking authority in medical contexts.This paper analyzes the application of XAI methods—particularly saliency aps—in medical image interpretation,identifies the underlying causes of spurious explanations,and proposes possible mitigation strategies.The aim is to contribute to the responsible and sustainable integration of explainable AI into clinical practice.展开更多
Given the importance of sentiment analysis in diverse environments,various methods are used for image sentiment analysis,including contextual sentiment analysis that utilizes character and scene relationships.However,...Given the importance of sentiment analysis in diverse environments,various methods are used for image sentiment analysis,including contextual sentiment analysis that utilizes character and scene relationships.However,most existing works employ character faces in conjunction with context,yet lack the capacity to analyze the emotions of characters in unconstrained environments,such as when their faces are obscured or blurred.Accordingly,this article presents the Adaptive Multi-Channel Sentiment Analysis Network(AMSA),a contextual image sentiment analysis framework,which consists of three channels:body,face,and context.AMSA employs Multi-task Cascaded Convolutional Networks(MTCNN)to detect faces within body frames;if detected,facial features are extracted and fused with body and context information for emotion recognition.If not,the model leverages body and context features alone.Meanwhile,to address class imbalance in the EMOTIC dataset,Focal Loss is introduced to improve classification performance,especially for minority emotion categories.Experimental results have shown that certain sentiment categories with lower representation in the dataset demonstrate leading classification accuracy,the AMSA yields a 2.53%increase compared with state-of-the-art methods.展开更多
The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial in...The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.展开更多
Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional imag...Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional images of specimens with single particle size of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10 ram. Based on the in-house developed 3D image analysis programs using Matlab, the volume porosity, pore size distribution and degree of connectivity were calculated and analyzed in detail. The results indicate that the volume porosity, the mean diameter of pores and the effective pore size （d50） increase with the increasing of particle size. Lognormal distribution or Gauss distribution is mostly suitable to model the pore size distribution. The degree of connectivity investigated on the basis of cluster-labeling algorithm also increases with increasing the particle size approximately.展开更多
Mineral dissemination and pore space distribution in ore particles are important features that influence heap leaching performance.To quantify the mineral dissemination and pore space distribution of an ore particle,a...Mineral dissemination and pore space distribution in ore particles are important features that influence heap leaching performance.To quantify the mineral dissemination and pore space distribution of an ore particle,a cylindrical copper oxide ore sample(I center dot 4.6 mm x 5.6 mm)was scanned using high-resolution X-ray computed tomography(HRXCT),a nondestructive imaging technology,at a spatial resolution of 4.85 mu m.Combined with three-dimensional(3D)image analysis techniques,the main mineral phases and pore space were segmented and the volume fraction of each phase was calculated.In addition,the mass fraction of each mineral phase was estimated and the result was validated with that obtained using traditional techniques.Furthermore,the pore phase features,including the pore size distribution,pore surface area,pore fractal dimension,pore centerline,and the pore connectivity,were investigated quantitatively.The pore space analysis results indicate that the pore size distribution closely fits a log-normal distribution and that the pore space morphology is complicated,with a large surface area and low connectivity.This study demonstrates that the combination of HRXCT and 3D image analysis is an effective tool for acquiring 3D mineralogical and pore structural data.展开更多
With the wide use of high-resolution remotely sensed imagery, the object-oriented remotely sensed informa- tion classification pattern has been intensively studied. Starting with the definition of object-oriented remo...With the wide use of high-resolution remotely sensed imagery, the object-oriented remotely sensed informa- tion classification pattern has been intensively studied. Starting with the definition of object-oriented remotely sensed information classification pattern and a literature review of related research progress, this paper sums up 4 developing phases of object-oriented classification pattern during the past 20 years. Then, we discuss the three aspects of method- ology in detail, namely remotely sensed imagery segmentation, feature analysis and feature selection, and classification rule generation, through comparing them with remotely sensed information classification method based on per-pixel. At last, this paper presents several points that need to be paid attention to in the future studies on object-oriented RS in- formation classification pattern: 1) developing robust and highly effective image segmentation algorithm for multi-spectral RS imagery; 2) improving the feature-set including edge, spatial-adjacent and temporal characteristics; 3) discussing the classification rule generation classifier based on the decision tree; 4) presenting evaluation methods for classification result by object-oriented classification pattern.展开更多
Based on the analysis of high-speed video images, the detachment behavior of dust cake from the ceramic candle filter surface during pulse cleaning process is investigated. The influences of the dust cake loading,the ...Based on the analysis of high-speed video images, the detachment behavior of dust cake from the ceramic candle filter surface during pulse cleaning process is investigated. The influences of the dust cake loading,the reservoir pressure, and the filtration velocity on the cleaning effectiveness are analyzed. Experimental results show that there exists an optimum dust cake thickness for pulse-cleaning process. For thin dust cake, the patchy cleaning exists and the cleaning efficiency is low; if the dust cake is too thick, the pressure drop across the dust cake becomes higher and a higher reservoir pressure may be needed. At the same time there also exists an optimum reservoir pressure for a given filtration condition.展开更多
It is critical to establish a direct and precise method with a high sensitivity and selectivity in analytical chemistry. In this research, making use of a well known phenomenon of capillary flow, we have proposed an...It is critical to establish a direct and precise method with a high sensitivity and selectivity in analytical chemistry. In this research, making use of a well known phenomenon of capillary flow, we have proposed an image analysis method of nucleic acids at the price of a small amount of sample. When a droplet of the supramolecular complex solution, formed by neutral red and nucleic acids(NA) under an approximate neutral condition, was placed on the hydrophobic surface of dimethyl dichlorosilane pretreated glass slides, and it was evaporated, the supramolecular complex exhibited the periphery of the droplet due to the capillary effect, and accumulated there to form a red capillary flow directed assembly ring(CFDAR). A typical CFDAR has an outer diameter of (2 r ) about 1.18 mm and a ring width(2 δ ) of about 41 μm. Depending on the experimental conditions, a variety of CFDAR can be assembled. The experimental results are in agreement with our former theoretical discussion. It was found that when a droplet volume is 0.1 μL, the fluorescence intensity of the CFDAR formed by the NR NA is in proportion to the content of calf thymus DNA in the range of 0-0.28 ng, fish sperm DNA of 0-0.24 ng and yeast RNA of 0-0.16 ng with the limit of detection(3 σ ) of 1 7, 1.4 and 0.9 pg, respectively for the three nucleic acids.展开更多
Unmanned aerial vehicle(UAV)-based imaging systems have many superiorities compared with other platforms,such as high flexibility and low cost in collecting images,providing wide application prospects.However,the acqu...Unmanned aerial vehicle(UAV)-based imaging systems have many superiorities compared with other platforms,such as high flexibility and low cost in collecting images,providing wide application prospects.However,the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images,which poses great challenges for subsequent applications.Therefore,an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time.In this work,we proposed a multi-scale hierarchical representation,i.e.binary partition tree,for analyzing large-scale UAV images.More precisely,we first obtained an initial partition of images by an oversegmentation algorithm,i.e.the simple linear iterative clustering.Next,we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships.Moreover,objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure.Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.展开更多
The particle morphology and surface texture play a major role in influencing mechanical and hydraulic behaviors of sandy soils. This paper presents the use of digital image analysis combined with fractal theory as a t...The particle morphology and surface texture play a major role in influencing mechanical and hydraulic behaviors of sandy soils. This paper presents the use of digital image analysis combined with fractal theory as a tool to quantify the particle morphology and surface texture of two types of quartz sands widely used in the region of Vitória, Espírito Santo, southeast of Brazil. The two investigated sands are sampled from different locations. The purpose of this paper is to present a simple, straightforward,reliable and reproducible methodology that can identify representative sandy soil texture parameters.The test results of the soil samples of the two sands separated by sieving into six size fractions are presented and discussed. The main advantages of the adopted methodology are its simplicity, reliability of the results, and relatively low cost. The results show that sands from the coastal spit(BS) have a greater degree of roundness and a smoother surface texture than river sands(RS). The values obtained in the test are statistically analyzed, and again it is confirmed that the BS sand has a slightly greater degree of sphericity than that of the RS sand. Moreover, the RS sand with rough surface texture has larger specific surface area values than the similar BS sand, which agree with the obtained roughness fractal dimensions. The consistent experimental results demonstrate that image analysis combined with fractal theory is an accurate and efficient method to quantify the differences in particle morphology and surface texture of quartz sands.展开更多
The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its...The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .展开更多
The degradation mechanisms of cementitious materials exposed to sulfate solutions have been controversial, despite considerable research. In this paper, two methodologies of image analysis based on scanning electron m...The degradation mechanisms of cementitious materials exposed to sulfate solutions have been controversial, despite considerable research. In this paper, two methodologies of image analysis based on scanning electron microscope and chemical mapping are used to analyse Portland cement mortars exposed to sodium sulfate solution. The effects of sulfate concentration in solution and water to cement ratio of mortar, which are considered as the most sensitive factors to sulfate attack, are investigated respectively by comparing the macro expansion with microstructure analysis. It is found that the sulfate concentration in pore solution, expressed as sulfate content in C-S-H, plays a critical role on the supersaturation with respect to ettringite and so on the expansion force generated.展开更多
Multi-modality medical image fusion has more and more important applications in medical image analysis and understanding. In this paper, we develop and apply a multi-resolution method based on wavelet pyramid to fuse ...Multi-modality medical image fusion has more and more important applications in medical image analysis and understanding. In this paper, we develop and apply a multi-resolution method based on wavelet pyramid to fuse medical images from different modalities such as PET-MRI and CT-MRI. In particular, we evaluate the different fusion results when applying different selection rules and obtain optimum combination of fusion parameters.展开更多
Corneal opacity is one of the most commonly used parameters for estimating postmortem interval （PMI）. This paper proposes a new method to study the relationship between changes of corneal opacity and PMI by processi...Corneal opacity is one of the most commonly used parameters for estimating postmortem interval （PMI）. This paper proposes a new method to study the relationship between changes of corneal opacity and PMI by processing and analyzing cornea images. Corneal regions were extracted from images of rabbits＇ eyes and described by color-based and texture-based features, which could represent the changes of cornea at different PMI. A KNN classifier was used to reveal the association of image features and PMI. The result of the classification showed that the new method was reliable and effective.展开更多
In this study, mitotic metaphase chromosomes in mouse were identified by a new chromosome fluorescence banding technique combining DAPI staining with image analysis. Clear 4＇, 6-diamidino-2-phenylindole （DAPI） mult...In this study, mitotic metaphase chromosomes in mouse were identified by a new chromosome fluorescence banding technique combining DAPI staining with image analysis. Clear 4＇, 6-diamidino-2-phenylindole （DAPI） multiple bands like （J-hands could be produced in mouse. The Meta- Morph software was then used to generate linescans of pixel intensity for the banded chromosomes from short arm to long arm. These linescans were sufficient not only to identify each individual chromosome but also analyze the physical sites of bands in chromosome. Based on the results, the clear and accurate karyotype of mouse metaphase chromosomes was established. The technique is therefore considered to he a new method for cytological studies of mouse.展开更多
Mammalian pelage color can vary among individuals of many species, although this intraspecific variation is oftenoverlooked by researchers, perhaps because of its sometimes subtle nature and difficulty in assessing it...Mammalian pelage color can vary among individuals of many species, although this intraspecific variation is oftenoverlooked by researchers, perhaps because of its sometimes subtle nature and difficulty in assessing it quantitatively. Thus, suchvariation is rarely studied in mammals, and this is especially true within the order Chiroptera, where there has been very little empiricalresearch. We examined museum specimens of red bats (Lasiurus borealis, family Vespertilionidae) from Georgia, USA, todetermine the extent of sexual dimorphism in pelage color and to explore possible associations between body size and pelagecolor. We photographed 54 specimens under uniform lighting, and used an image analysis program to measure pelage hue on theuropatagium region, which is fully furred in members of the genus Lasiurus. Statistical analyses of pelage hue scores showedmales had significantly redder pelage than females when considered alone, but when examined together with effects of body sizeand collection year, sex was not significant, and collection year and body size were. More recent specimens tended to be less redthan older specimens, which might indicate a wearing of the buffy tips of hairs from older specimens, and smaller bats of bothsexes tended to be more red. These interesting findings are encouraging and we suggest that future explorations into intraspecificvariation in pelage color of bats using this or similar approaches are warranted to clarify the significance of the patterns. Thisstudy also demonstrated that care must be taken in analyses of mammalian pelage color from older museum skins, or at least thatresearchers must take into account the age of the specimens .展开更多
Many methods based on deep learning have achieved amazing results in image sentiment analysis.However,these existing methods usually pursue high accuracy,ignoring the effect on model training efficiency.Considering th...Many methods based on deep learning have achieved amazing results in image sentiment analysis.However,these existing methods usually pursue high accuracy,ignoring the effect on model training efficiency.Considering that when faced with large-scale sentiment analysis tasks,the high accuracy rate often requires long experimental time.In view of the weakness,a method that can greatly improve experimental efficiency with only small fluctuations in model accuracy is proposed,and singular value decomposition(SVD)is used to find the sparse feature of the image,which are sparse vectors with strong discriminativeness and effectively reduce redundant information;The authors propose the Fast Dictionary Learning algorithm(FDL),which can combine neural network with sparse representation.This method is based on K-Singular Value Decomposition,and through iteration,it can effectively reduce the calculation time and greatly improve the training efficiency in the case of small fluctuation of accuracy.Moreover,the effectiveness of the proposed method is evaluated on the FER2013 dataset.By adding singular value decomposition,the accuracy of the test suite increased by 0.53%,and the total experiment time was shortened by 8.2%;Fast Dictionary Learning shortened the total experiment time by 36.3%.展开更多
基金supported by the Deanship of Research and Graduate Studies at King Khalid University under Small Research Project grant number RGP1/139/45.
文摘Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.
基金Supported by Grant-in-Aid for Research on Hepatitis from the Japan Agency for Medical Research and Development,No.24fk0210128h0002Grant-in-Aid for Scientific Research,No.KAKENHI-23K07372.
文摘BACKGROUND Congestive hepatopathy,also known as nutmeg liver,is liver damage secondary to chronic heart failure(HF).Its morphological characteristics in terms of medical imaging are not defined and remain unclear.AIM To leverage machine learning to capture imaging features of congestive hepatopathy using incidentally acquired computed tomography(CT)scans.METHODS We retrospectively analyzed 179 chronic HF patients who underwent echocardiography and CT within one year.Right HF severity was classified into three grades.Liver CT images at the paraumbilical vein level were used to develop a ResNet-based machine learning model to predict tricuspid regurgitation(TR)severity.Model accuracy was compared with that of six gastroenterology and four radiology experts.RESULTS In the included patients,120 were male(mean age:73.1±14.4 years).The accuracy of the results predicting TR severity from a single CT image for the machine learning model was significantly higher than the average accuracy of the experts.The model was found to be exceptionally reliable for predicting severe TR.CONCLUSION Deep learning models,particularly those using ResNet architectures,can help identify morphological changes associated with TR severity,aiding in early liver dysfunction detection in patients with HF,thereby improving outcomes.
文摘Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have made early contributions;however,recent advancements in deep learning(DL)have revolutionized the field,offering state-of-the-art performance in image classification,segmentation,detection,fusion,registration,and enhancement.This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks,highlighting both foundational models and recent innovations.The article begins by introducing conventional techniques and their limitations,setting the stage for DL-based solutions.Core DL architectures,including Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),Generative Adversarial Networks(GANs),Vision Transformers(ViTs),and hybrid models,are discussed in detail,including their advantages and domain-specific adaptations.Advanced learning paradigms such as semi-supervised learning,selfsupervised learning,and few-shot learning are explored for their potential to mitigate data annotation challenges in clinical datasets.This review further categorizes major tasks in medical image analysis,elaborating on how DL techniques have enabled precise tumor segmentation,lesion detection,modality fusion,super-resolution,and robust classification across diverse clinical settings.Emphasis is placed on applications in oncology,cardiology,neurology,and infectious diseases,including COVID-19.Challenges such as data scarcity,label imbalance,model generalizability,interpretability,and integration into clinical workflows are critically examined.Ethical considerations,explainable AI(XAI),federated learning,and regulatory compliance are discussed as essential components of real-world deployment.Benchmark datasets,evaluation metrics,and comparative performance analyses are presented to support future research.The article concludes with a forward-looking perspective on the role of foundation models,multimodal learning,edge AI,and bio-inspired computing in the future of medical imaging.Overall,this review serves as a valuable resource for researchers,clinicians,and developers aiming to harness deep learning for intelligent,efficient,and clinically viable medical image analysis.
文摘Deep learning models have become a core technological tool in the field of medical image analysis.However,these models often suffer from a lack of transparency in their decision-making processes,leading to challenges related to trust and interpret ability in clinical applications.To address this issue,explainable artificial intelligence(XAI)techniques have been applied to medical image analysis.While showing promising potential,XAI also brings significant ethical risks in practice—most notably,the problem of spurious explanations.Such explanations may rise further concerns regarding patient privacy,data security,and the attribution of decisionmaking authority in medical contexts.This paper analyzes the application of XAI methods—particularly saliency aps—in medical image interpretation,identifies the underlying causes of spurious explanations,and proposes possible mitigation strategies.The aim is to contribute to the responsible and sustainable integration of explainable AI into clinical practice.
文摘Given the importance of sentiment analysis in diverse environments,various methods are used for image sentiment analysis,including contextual sentiment analysis that utilizes character and scene relationships.However,most existing works employ character faces in conjunction with context,yet lack the capacity to analyze the emotions of characters in unconstrained environments,such as when their faces are obscured or blurred.Accordingly,this article presents the Adaptive Multi-Channel Sentiment Analysis Network(AMSA),a contextual image sentiment analysis framework,which consists of three channels:body,face,and context.AMSA employs Multi-task Cascaded Convolutional Networks(MTCNN)to detect faces within body frames;if detected,facial features are extracted and fused with body and context information for emotion recognition.If not,the model leverages body and context features alone.Meanwhile,to address class imbalance in the EMOTIC dataset,Focal Loss is introduced to improve classification performance,especially for minority emotion categories.Experimental results have shown that certain sentiment categories with lower representation in the dataset demonstrate leading classification accuracy,the AMSA yields a 2.53%increase compared with state-of-the-art methods.
基金support from the National Key Research and Development Program of China(No.2024YFB3713705)is acknowledgedWangzhong Mu would like to acknowledge the Strategic Mobility,Sweden(SSF,No.SM22-0039)+1 种基金the Swedish Foundation for International Cooperation in Research and Higher Education(STINT,No.IB2022-9228)the Jernkontoret(Sweden)for supporting this clean steel research.Gonghao Lian would like to acknowledge China Scholarship Council(CSC,No.202306080032).
文摘The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.
基金Projects(50934002,51074013,51304076,51104100)supported by the National Natural Science Foundation of ChinaProject(IRT0950)supported by the Program for Changjiang Scholars Innovative Research Team in Universities,ChinaProject(2012M510007)supported by China Postdoctoral Science Foundation
文摘Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional images of specimens with single particle size of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10 ram. Based on the in-house developed 3D image analysis programs using Matlab, the volume porosity, pore size distribution and degree of connectivity were calculated and analyzed in detail. The results indicate that the volume porosity, the mean diameter of pores and the effective pore size （d50） increase with the increasing of particle size. Lognormal distribution or Gauss distribution is mostly suitable to model the pore size distribution. The degree of connectivity investigated on the basis of cluster-labeling algorithm also increases with increasing the particle size approximately.
基金financially supported by the National Natural Science Foundation of China(No.51304076)the Natural Science Foundation of Hunan Province,China(No.14JJ4064)
文摘Mineral dissemination and pore space distribution in ore particles are important features that influence heap leaching performance.To quantify the mineral dissemination and pore space distribution of an ore particle,a cylindrical copper oxide ore sample(I center dot 4.6 mm x 5.6 mm)was scanned using high-resolution X-ray computed tomography(HRXCT),a nondestructive imaging technology,at a spatial resolution of 4.85 mu m.Combined with three-dimensional(3D)image analysis techniques,the main mineral phases and pore space were segmented and the volume fraction of each phase was calculated.In addition,the mass fraction of each mineral phase was estimated and the result was validated with that obtained using traditional techniques.Furthermore,the pore phase features,including the pore size distribution,pore surface area,pore fractal dimension,pore centerline,and the pore connectivity,were investigated quantitatively.The pore space analysis results indicate that the pore size distribution closely fits a log-normal distribution and that the pore space morphology is complicated,with a large surface area and low connectivity.This study demonstrates that the combination of HRXCT and 3D image analysis is an effective tool for acquiring 3D mineralogical and pore structural data.
基金Under the auspices of the National Natural Science Foundation of China (No. 40301038), Talents Recruitment Foun-dation of Nanjing University
文摘With the wide use of high-resolution remotely sensed imagery, the object-oriented remotely sensed informa- tion classification pattern has been intensively studied. Starting with the definition of object-oriented remotely sensed information classification pattern and a literature review of related research progress, this paper sums up 4 developing phases of object-oriented classification pattern during the past 20 years. Then, we discuss the three aspects of method- ology in detail, namely remotely sensed imagery segmentation, feature analysis and feature selection, and classification rule generation, through comparing them with remotely sensed information classification method based on per-pixel. At last, this paper presents several points that need to be paid attention to in the future studies on object-oriented RS in- formation classification pattern: 1) developing robust and highly effective image segmentation algorithm for multi-spectral RS imagery; 2) improving the feature-set including edge, spatial-adjacent and temporal characteristics; 3) discussing the classification rule generation classifier based on the decision tree; 4) presenting evaluation methods for classification result by object-oriented classification pattern.
基金Supported by the National Natural Science Foundation of China (No. 50376042)Doctoral Program Foundation of Institute of Higher Education of China (20040425007).
文摘Based on the analysis of high-speed video images, the detachment behavior of dust cake from the ceramic candle filter surface during pulse cleaning process is investigated. The influences of the dust cake loading,the reservoir pressure, and the filtration velocity on the cleaning effectiveness are analyzed. Experimental results show that there exists an optimum dust cake thickness for pulse-cleaning process. For thin dust cake, the patchy cleaning exists and the cleaning efficiency is low; if the dust cake is too thick, the pressure drop across the dust cake becomes higher and a higher reservoir pressure may be needed. At the same time there also exists an optimum reservoir pressure for a given filtration condition.
基金Supported by the NationalNaturalScience Foundation of China( No. 2 0 175 0 1) and U niversity Key Teachers Programdirected under the Ministry of Education ofP.R.China( No. 2 0 0 0 - 6 5 )
文摘It is critical to establish a direct and precise method with a high sensitivity and selectivity in analytical chemistry. In this research, making use of a well known phenomenon of capillary flow, we have proposed an image analysis method of nucleic acids at the price of a small amount of sample. When a droplet of the supramolecular complex solution, formed by neutral red and nucleic acids(NA) under an approximate neutral condition, was placed on the hydrophobic surface of dimethyl dichlorosilane pretreated glass slides, and it was evaporated, the supramolecular complex exhibited the periphery of the droplet due to the capillary effect, and accumulated there to form a red capillary flow directed assembly ring(CFDAR). A typical CFDAR has an outer diameter of (2 r ) about 1.18 mm and a ring width(2 δ ) of about 41 μm. Depending on the experimental conditions, a variety of CFDAR can be assembled. The experimental results are in agreement with our former theoretical discussion. It was found that when a droplet volume is 0.1 μL, the fluorescence intensity of the CFDAR formed by the NR NA is in proportion to the content of calf thymus DNA in the range of 0-0.28 ng, fish sperm DNA of 0-0.24 ng and yeast RNA of 0-0.16 ng with the limit of detection(3 σ ) of 1 7, 1.4 and 0.9 pg, respectively for the three nucleic acids.
基金This work was supported in part by the National Key Basic Research and Development Program of China[grant number 2013CB733404]the National Natural Science Foundation of China[grant number 61271401],[grant number 91338113].
文摘Unmanned aerial vehicle(UAV)-based imaging systems have many superiorities compared with other platforms,such as high flexibility and low cost in collecting images,providing wide application prospects.However,the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images,which poses great challenges for subsequent applications.Therefore,an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time.In this work,we proposed a multi-scale hierarchical representation,i.e.binary partition tree,for analyzing large-scale UAV images.More precisely,we first obtained an initial partition of images by an oversegmentation algorithm,i.e.the simple linear iterative clustering.Next,we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships.Moreover,objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure.Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.
文摘The particle morphology and surface texture play a major role in influencing mechanical and hydraulic behaviors of sandy soils. This paper presents the use of digital image analysis combined with fractal theory as a tool to quantify the particle morphology and surface texture of two types of quartz sands widely used in the region of Vitória, Espírito Santo, southeast of Brazil. The two investigated sands are sampled from different locations. The purpose of this paper is to present a simple, straightforward,reliable and reproducible methodology that can identify representative sandy soil texture parameters.The test results of the soil samples of the two sands separated by sieving into six size fractions are presented and discussed. The main advantages of the adopted methodology are its simplicity, reliability of the results, and relatively low cost. The results show that sands from the coastal spit(BS) have a greater degree of roundness and a smoother surface texture than river sands(RS). The values obtained in the test are statistically analyzed, and again it is confirmed that the BS sand has a slightly greater degree of sphericity than that of the RS sand. Moreover, the RS sand with rough surface texture has larger specific surface area values than the similar BS sand, which agree with the obtained roughness fractal dimensions. The consistent experimental results demonstrate that image analysis combined with fractal theory is an accurate and efficient method to quantify the differences in particle morphology and surface texture of quartz sands.
基金This work was supported by Science and Technology Project of State Grid Corporation“Research on Key Technologies of Power Artificial Intelligence Open Platform”(5700-202155260A-0-0-00).
文摘The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .
基金Founded by National Basic Research Program of China(973 Program)(No.2009CB623203)National Natural Science Foundation of China(No.51078186)Jiangsu Natural Science Foundation(No.BK2010071)
文摘The degradation mechanisms of cementitious materials exposed to sulfate solutions have been controversial, despite considerable research. In this paper, two methodologies of image analysis based on scanning electron microscope and chemical mapping are used to analyse Portland cement mortars exposed to sodium sulfate solution. The effects of sulfate concentration in solution and water to cement ratio of mortar, which are considered as the most sensitive factors to sulfate attack, are investigated respectively by comparing the macro expansion with microstructure analysis. It is found that the sulfate concentration in pore solution, expressed as sulfate content in C-S-H, plays a critical role on the supersaturation with respect to ettringite and so on the expansion force generated.
基金the National Natural Science Foundation of China (No. 19675005).
文摘Multi-modality medical image fusion has more and more important applications in medical image analysis and understanding. In this paper, we develop and apply a multi-resolution method based on wavelet pyramid to fuse medical images from different modalities such as PET-MRI and CT-MRI. In particular, we evaluate the different fusion results when applying different selection rules and obtain optimum combination of fusion parameters.
文摘Corneal opacity is one of the most commonly used parameters for estimating postmortem interval （PMI）. This paper proposes a new method to study the relationship between changes of corneal opacity and PMI by processing and analyzing cornea images. Corneal regions were extracted from images of rabbits＇ eyes and described by color-based and texture-based features, which could represent the changes of cornea at different PMI. A KNN classifier was used to reveal the association of image features and PMI. The result of the classification showed that the new method was reliable and effective.
文摘In this study, mitotic metaphase chromosomes in mouse were identified by a new chromosome fluorescence banding technique combining DAPI staining with image analysis. Clear 4＇, 6-diamidino-2-phenylindole （DAPI） multiple bands like （J-hands could be produced in mouse. The Meta- Morph software was then used to generate linescans of pixel intensity for the banded chromosomes from short arm to long arm. These linescans were sufficient not only to identify each individual chromosome but also analyze the physical sites of bands in chromosome. Based on the results, the clear and accurate karyotype of mouse metaphase chromosomes was established. The technique is therefore considered to he a new method for cytological studies of mouse.
基金supported by a grant rrom the Morris Animal Foundation
文摘Mammalian pelage color can vary among individuals of many species, although this intraspecific variation is oftenoverlooked by researchers, perhaps because of its sometimes subtle nature and difficulty in assessing it quantitatively. Thus, suchvariation is rarely studied in mammals, and this is especially true within the order Chiroptera, where there has been very little empiricalresearch. We examined museum specimens of red bats (Lasiurus borealis, family Vespertilionidae) from Georgia, USA, todetermine the extent of sexual dimorphism in pelage color and to explore possible associations between body size and pelagecolor. We photographed 54 specimens under uniform lighting, and used an image analysis program to measure pelage hue on theuropatagium region, which is fully furred in members of the genus Lasiurus. Statistical analyses of pelage hue scores showedmales had significantly redder pelage than females when considered alone, but when examined together with effects of body sizeand collection year, sex was not significant, and collection year and body size were. More recent specimens tended to be less redthan older specimens, which might indicate a wearing of the buffy tips of hairs from older specimens, and smaller bats of bothsexes tended to be more red. These interesting findings are encouraging and we suggest that future explorations into intraspecificvariation in pelage color of bats using this or similar approaches are warranted to clarify the significance of the patterns. Thisstudy also demonstrated that care must be taken in analyses of mammalian pelage color from older museum skins, or at least thatresearchers must take into account the age of the specimens .
基金supported by the National Natural Science Foundation of China(No.61801440)the High‐quality and Cutting‐edge Disciplines Construction Project for Universities in Beijing(Internet Information,Communication University of China),State Key Laboratory of Media Convergence and Communication(Communication University of China)the Fundamental Research Funds for the Central Universities(CUC2019B069).
文摘Many methods based on deep learning have achieved amazing results in image sentiment analysis.However,these existing methods usually pursue high accuracy,ignoring the effect on model training efficiency.Considering that when faced with large-scale sentiment analysis tasks,the high accuracy rate often requires long experimental time.In view of the weakness,a method that can greatly improve experimental efficiency with only small fluctuations in model accuracy is proposed,and singular value decomposition(SVD)is used to find the sparse feature of the image,which are sparse vectors with strong discriminativeness and effectively reduce redundant information;The authors propose the Fast Dictionary Learning algorithm(FDL),which can combine neural network with sparse representation.This method is based on K-Singular Value Decomposition,and through iteration,it can effectively reduce the calculation time and greatly improve the training efficiency in the case of small fluctuation of accuracy.Moreover,the effectiveness of the proposed method is evaluated on the FER2013 dataset.By adding singular value decomposition,the accuracy of the test suite increased by 0.53%,and the total experiment time was shortened by 8.2%;Fast Dictionary Learning shortened the total experiment time by 36.3%.
