With the development of anti-virus technology,malicious documents have gradually become the main pathway of Advanced Persistent Threat(APT)attacks,therefore,the development of effective malicious document classifiers ...With the development of anti-virus technology,malicious documents have gradually become the main pathway of Advanced Persistent Threat(APT)attacks,therefore,the development of effective malicious document classifiers has become particularly urgent.Currently,detection methods based on document structure and behavioral features encounter challenges in feature engineering,these methods not only have limited accuracy,but also consume large resources,and usually can only detect documents in specific formats,which lacks versatility and adaptability.To address such problems,this paper proposes a novel malicious document detection method-visualizing documents as GGE images(Grayscale,Grayscale matrix,Entropy).The GGE method visualizes the original byte sequence of the malicious document as a grayscale image,the information entropy sequence of the document as an entropy image,and at the same time,the grayscale level co-occurrence matrix and the texture and spatial information stored in it are converted into grayscale matrix image,and fuses the three types of images to get the GGE color image.The Convolutional Block Attention Module-EfficientNet-B0(CBAM-EfficientNet-B0)model is then used for classification,combining transfer learning and applying the pre-trained model on the ImageNet dataset to the feature extraction process of GGE images.As shown in the experimental results,the GGE method has superior performance compared with other methods,which is suitable for detecting malicious documents in different formats,and achieves an accuracy of 99.44%and 97.39%on Portable Document Format(PDF)and office datasets,respectively,and consumes less time during the detection process,which can be effectively applied to the task of detecting malicious documents in real-time.展开更多
BACKGROUND Focal nodular hyperplasia(FNH)-like lesions are hyperplastic formations in patients with micronodular cirrhosis and a history of alcohol abuse.Although pathologically similar to hepatocellular carcinoma(HCC...BACKGROUND Focal nodular hyperplasia(FNH)-like lesions are hyperplastic formations in patients with micronodular cirrhosis and a history of alcohol abuse.Although pathologically similar to hepatocellular carcinoma(HCC)lesions,they are benign.As such,it is important to develop methods to distinguish between FNH-like lesions and HCC.AIM To evaluate diagnostically differential radiological findings between FNH-like lesions and HCC.METHODS We studied pathologically confirmed FNH-like lesions in 13 patients with alco-holic cirrhosis[10 men and 3 women;mean age:54.5±12.5(33-72)years]who were negative for hepatitis-B surface antigen and hepatitis-C virus antibody and underwent dynamic computed tomography(CT)and magnetic resonance imaging(MRI),including superparamagnetic iron oxide(SPIO)and/or gadoxetic acid-enhanced MRI.Seven patients also underwent angiography-assisted CT.RESULTS The evaluated lesion features included arterial enhancement pattern,washout appearance(low density compared with that of surrounding liver parenchyma),signal intensity on T1-weighted image(T1WI)and T2-weighted image(T2WI),central scar presence,chemical shift on in-and out-of-phase images,and uptake pattern on gadoxetic acid-enhanced MRI hepatobiliary phase and SPIO-enhanced MRI.Eleven patients had multiple small lesions(<1.5 cm).Radiological features of FNH-like lesions included hypervascularity despite small lesions,lack of“corona-like”enhancement in the late phase on CT during hepatic angiography(CTHA),high-intensity on T1WI,slightly high-or iso-intensity on T2WI,no signal decrease in out-of-phase images,and complete SPIO uptake or incomplete/partial uptake of gadoxetic acid.Pathologically,similar to HCC,FNH-like lesions showed many unpaired arteries and sinusoidal capillarization.CONCLUSION Overall,the present study showed that FNH-like lesions have unique radiological findings useful for differential diagnosis.Specifically,SPIO-and/or gadoxetic acid-enhanced MRI and CTHA features might facilitate differential diagnosis of FNH-like lesions and HCC.展开更多
The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for ident...The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for identifying and mapping the quality of these herbal medicines.This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs.It focuses on feature extraction methods,image processing techniques,and the preparation of herbal images for compatibility with machine learning models.The article discusses commonly used image processing tools such as normalization,slicing,cropping,and augmentation to prepare images for artificial intelligence-based models.It also provides an overview of global herbal image databases and the models employed for herbal plant/drug identification.Readers will gain a comprehensive understanding of the potential application of various machine learning models,including artificial neural networks and convolutional neural networks.The article delves into suitable validation parameters like true positive rates,accuracy,precision,and more for the development of artificial intelligence-based identification and authentication techniques for herbal drugs.This article offers valuable insights and a conclusive platform for the further exploration of artificial intelligence in the field of herbal drugs,paving the way for smarter identification and authentication methods.展开更多
Brain tumor segmentation is critical in clinical diagnosis and treatment planning.Existing methods for brain tumor segmentation with missing modalities often struggle when dealing with multiple missing modalities,a co...Brain tumor segmentation is critical in clinical diagnosis and treatment planning.Existing methods for brain tumor segmentation with missing modalities often struggle when dealing with multiple missing modalities,a common scenario in real-world clinical settings.These methods primarily focus on handling a single missing modality at a time,making them insufficiently robust for the additional complexity encountered with incomplete data containing various missing modality combinations.Additionally,most existing methods rely on single models,which may limit their performance and increase the risk of overfitting the training data.This work proposes a novel method called the ensemble adversarial co-training neural network(EACNet)for accurate brain tumor segmentation from multi-modal magnetic resonance imaging(MRI)scans with multiple missing modalities.The proposed method consists of three key modules:the ensemble of pre-trained models,which captures diverse feature representations from the MRI data by employing an ensemble of pre-trained models;adversarial learning,which leverages a competitive training approach involving two models;a generator model,which creates realistic missing data,while sub-networks acting as discriminators learn to distinguish real data from the generated“fake”data.Co-training framework utilizes the information extracted by the multimodal path(trained on complete scans)to guide the learning process in the path handling missing modalities.The model potentially compensates for missing information through co-training interactions by exploiting the relationships between available modalities and the tumor segmentation task.EACNet was evaluated on the BraTS2018 and BraTS2020 challenge datasets and achieved state-of-the-art and competitive performance respectively.Notably,the segmentation results for the whole tumor(WT)dice similarity coefficient(DSC)reached 89.27%,surpassing the performance of existing methods.The analysis suggests that the ensemble approach offers potential benefits,and the adversarial co-training contributes to the increased robustness and accuracy of EACNet for brain tumor segmentation of MRI scans with missing modalities.The experimental results show that EACNet has promising results for the task of brain tumor segmentation of MRI scans with missing modalities and is a better candidate for real-world clinical applications.展开更多
Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for glo...Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for global food security and ecological balance.Currently,traditional detection strategies for monitoring plant health mainly rely on expensive equipment and complex operational procedures,which limit their widespread application.Fortunately,near-infrared(NIR)fluorescence and surface-enhanced Raman scattering(SERS)techniques have been recently highlighted in plants.NIR fluorescence imaging holds the advantages of being non-invasive,high-resolution and real-time,which is suitable for rapid screening in large-scale scenarios.While SERS enables highly sensitive and specific detection of trace chemical substances within plant tissues.Therefore,the complementarity of NIR fluorescence and SERS modalities can provide more comprehensive and accurate information for plant disease diagnosis and growth status monitoring.This article summarizes these two modalities in plant applications,and discusses the advantages of multimodal NIR fluorescence/SERS for a better understanding of a plant’s response to stress,thereby improving the accuracy and sensitivity of detection.展开更多
Domain generalizable person re-identification(reid)is a challenging task in computer vision,which aims to apply a trained reid model to unseen domains.Prior works either combine the data in all the training domains to...Domain generalizable person re-identification(reid)is a challenging task in computer vision,which aims to apply a trained reid model to unseen domains.Prior works either combine the data in all the training domains to capture domain-invariant features,or adopt a mixture of experts to investigate domain-specific information.In this work,we argue that both domain-specific and domain-invariant features are crucial for improving the generalization ability of reid models.To this end,we design a novel framework,which we name two-stream adaptive learning(TAL),to simultaneously model these two kinds of information.Specifically,a domain-specific stream is proposed to capture the training domain statistics with batch normalization(BN)parameters,whereas an adaptive matching layer is designed to dynamically aggregate domain-level information.In the meantime,we design an adaptive BN layer in the domain-invariant stream to approximate the statistic of unseen domains,such that our model is capable of handling various novel scenes.These two streams work adaptively and collaboratively to learn generalizable reid features.As validated by extensive experiments,our framework can be applied to both single-source and multi-source domain generalization tasks,where the results show that our framework notably outperforms the state-of-the-art methods.展开更多
Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing...Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing tools.The manual forgery localization is often reliant on forensic expertise.In recent times,machine learning(ML)and deep learning(DL)have shown promising results in automating image forgery localization.However,the ML-based method relies on hand-crafted features.Conversely,the DL method automatically extracts shallow spatial features to enhance the accuracy.However,DL-based methods lack the global co-relation of the features due to this performance degradation noticed in several applications.In the proposed study,we designed FLTNet(forgery localization transformer network)with a CNN(convolution neural network)encoder and transformer-based attention.The encoder extracts local high-dimensional features,and the transformer provides the global co-relation of the features.In the decoder,we have exclusively utilized a CNN to upsample the features that generate tampered mask images.Moreover,we evaluated visual and quantitative performance on three standard datasets and comparison with six state-of-the-art methods.The IoU values of the proposed method on CASIA V1,CASIA V2,and CoMoFoD datasets are 0.77,0.82,and 0.84,respectively.In addition,the F1-scores of these three datasets are 0.80,0.84,and 0.86,respectively.Furthermore,the visual results of the proposed method are clean and contain rich information,which can be used for real-time forgery detection.The code used in the study can be accessed through URL:https://github.com/ajit2k5/Forgery-Localization(accessed on 21 January 2025).展开更多
In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when fa...In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when faced with testing scenarios from unknown domains.To address this problem,this paper proposes a novel semi-supervised approach for cardiac magnetic resonance image segmentation,aiming to enhance predictive capabilities and domain generalization(DG).This paper establishes an MT-like model utilizing pseudo-labeling and consistency regularization from semi-supervised learning,and integrates uncertainty estimation to improve the accuracy of pseudo-labels.Additionally,to tackle the challenge of domain generalization,a data manipulation strategy is introduced,extracting spatial and content-related information from images across different domains,enriching the dataset with a multi-domain perspective.This papers method is meticulously evaluated on the publicly available cardiac magnetic resonance imaging dataset M&Ms,validating its effectiveness.Comparative analyses against various methods highlight the out-standing performance of this papers approach,demonstrating its capability to segment cardiac magnetic resonance images in previously unseen domains even with limited annotated data.展开更多
Recent deep neural network(DNN)based blind image quality assessment(BIQA)approaches take mean opinion score(MOS)as ground-truth labels,which would lead to cross-datasets biases and limited generalization ability of th...Recent deep neural network(DNN)based blind image quality assessment(BIQA)approaches take mean opinion score(MOS)as ground-truth labels,which would lead to cross-datasets biases and limited generalization ability of the DNN-based BIQA model.This work validates the natural instability of MOS through investigating the neuropsychological characteristics inside the human visual system during quality perception.By combining persistent homology analysis with electroencephalogram(EEG),the physiologically meaningful features of the brain responses to different distortion levels are extracted.The physiological features indicate that although volunteers view exactly the same image content,their EEG features are quite varied.Based on the physiological results,we advocate treating MOS as noisy labels and optimizing the DNN based BIQA model with earlystop strategies.Experimental results on both innerdataset and cross-dataset demonstrate the superiority of our optimization approach in terms of generalization ability.展开更多
Creating conditions to implement equilibrium processes of damage accumulation under a predictable scenario enables control over the failure of structural elements in critical states.It improves safety and reduces the ...Creating conditions to implement equilibrium processes of damage accumulation under a predictable scenario enables control over the failure of structural elements in critical states.It improves safety and reduces the probability of catastrophic behavior in case of accidents.Equilibrium damage accumulation in some cases leads to a falling part(called a postcritical stage)on the material’s stress-strain curve.It must be taken into account to assess the strength and deformation limits of composite structures.Digital image correlation method,acoustic emission(AE)signals recording,and optical microscopy were used in this paper to study the deformation and failure processes of an orthogonal-layup composite during tension in various directions to orthotropy axes.An elastic-plastic deformation model was proposed for the composite in a plane stress condition.The evolution of strain fields and neck formation were analyzed.The staging of the postcritical deformation process was described.AE signals obtained during tests were studied;characteristic damage types of a material were defined.The rationality and necessity of polymer composites’postcritical deformation stage taken into account in refined strength analysis of structures were concluded.展开更多
BACKGROUND Major depressive disorder(MDD)with comorbid anxiety is an intricate psychiatric condition,but limited research is available on the degree centrality(DC)between anxious MDD and nonanxious MDD patients.AIM To...BACKGROUND Major depressive disorder(MDD)with comorbid anxiety is an intricate psychiatric condition,but limited research is available on the degree centrality(DC)between anxious MDD and nonanxious MDD patients.AIM To examine changes in DC values and their use as neuroimaging biomarkers in anxious and non-anxious MDD patients.METHODS We examined 23 anxious MDD patients,30 nonanxious MDD patients,and 28 healthy controls(HCs)using the DC for data analysis.RESULTS Compared with HCs,the anxious MDD group reported markedly reduced DC values in the right fusiform gyrus(FFG)and inferior occipital gyrus,whereas elevated DC values in the left middle frontal gyrus and left inferior parietal angular gyrus.The nonanxious MDD group exhibited surged DC values in the bilateral cerebellum IX,right precuneus,and opercular part of the inferior frontal gyrus.Unlike the nonanxious MDD group,the anxious MDD group exhibited declined DC values in the right FFG and bilateral calcarine(CAL).Besides,declined DC values in the right FFG and bilateral CAL negatively correlated with anxiety scores in the MDD group.CONCLUSION This study shows that abnormal DC patterns in MDD,especially in the left CAL,can distinguish MDD from its anxiety subtype,indicating a potential neuroimaging biomarker.展开更多
In this paper,through the use of image space analysis,optimality conditions for a class of variational inequalities with cone constraints are proposed.By virtue of the nonlinear scalarization function,known as the Ger...In this paper,through the use of image space analysis,optimality conditions for a class of variational inequalities with cone constraints are proposed.By virtue of the nonlinear scalarization function,known as the Gerstewitz function,three nonlinear weak separation functions,two nonlinear regular weak separation functions and a nonlinear strong separation function are introduced.According to nonlinearseparation functions,some optimality conditions of the weak and strong alternative for variational inequalities with cone constraints are derived.展开更多
Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective intervention.While Deep Learning(DL)approaches have shown p...Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective intervention.While Deep Learning(DL)approaches have shown promise in AD diagnosis,existing methods often struggle with the issues of precision,interpretability,and class imbalance.This study presents a novel framework that integrates DL with several eXplainable Artificial Intelligence(XAI)techniques,in particular attention mechanisms,Gradient-Weighted Class Activation Mapping(Grad-CAM),and Local Interpretable Model-Agnostic Explanations(LIME),to improve bothmodel interpretability and feature selection.The study evaluates four different DL architectures(ResMLP,VGG16,Xception,and Convolutional Neural Network(CNN)with attention mechanism)on a balanced dataset of 3714 MRI brain scans from patients aged 70 and older.The proposed CNN with attention model achieved superior performance,demonstrating 99.18%accuracy on the primary dataset and 96.64% accuracy on the ADNI dataset,significantly advancing the state-of-the-art in AD classification.The ability of the framework to provide comprehensive,interpretable results through multiple visualization techniques while maintaining high classification accuracy represents a significant advancement in the computational diagnosis of AD,potentially enabling more accurate and earlier intervention in clinical settings.展开更多
In order to evaluate radiometric normalization techniques, two image normalization algorithms for absolute radiometric correction of Landsat imagery were quantitatively compared in this paper, which are the Illuminati...In order to evaluate radiometric normalization techniques, two image normalization algorithms for absolute radiometric correction of Landsat imagery were quantitatively compared in this paper, which are the Illumination Correction Model proposed by Markham and Irish and the Illumination and Atmospheric Correction Model developed by the Remote Sensing and GIS Laboratory of the Utah State University. Relative noise, correlation coefficient and slope value were used as the criteria for the evaluation and comparison, which were derived from pseudo-invarlant features identified from multitemporal Landsat image pairs of Xiamen （厦门） and Fuzhou （福州） areas, both located in the eastern Fujian （福建） Province of China. Compared with the unnormalized image, the radiometric differences between the normalized multitemporal images were significantly reduced when the seasons of multitemporal images were different. However, there was no significant difference between the normalized and unnorrealized images with a similar seasonal condition. Furthermore, the correction results of two algorithms are similar when the images are relatively clear with a uniform atmospheric condition. Therefore, the radiometric normalization procedures should be carried out if the multitemporal images have a significant seasonal difference.展开更多
The NW part of Iran belongs to the Iranian plateau that is a tectonically active region within the Alpine-Himalayan orogenic belt. The intrusion of Oligocene parts in various faces caused the alteration and mineraliza...The NW part of Iran belongs to the Iranian plateau that is a tectonically active region within the Alpine-Himalayan orogenic belt. The intrusion of Oligocene parts in various faces caused the alteration and mineralization such as copper, molybdenum, gold and iron in the Siyahrood area. Granitoidic rocks with component of Granodiorite to alkali have been influenced by hydrothermal fluids. Alteration zones are important features for the exploration of deposits and the ASTER sensor is able to identify the type of alteration and its alteration zoning. This method can be a useful tool for detecting potential mineralization area in East Azarbaijan—Northwest of Iran. The purpose of this study is to evaluate ASTER data for mapping altered minerals in Siyahrood area in order to detect the potential mineralized areas. In this study, false color composite, and band ratio techniques were applied on ASTER data and argillic, phyllic, Iron oxide and propylitic alteration zones were separated. ASAR image processing has been used for lineaments and faults identified by the aid of directional filter. The structural study focused on fracture zones and their characteristics including strike, length, and relationship with alteration zones. The results of this study demonstrate the usefulness of remote sensing methods and ASTER multi-spectral data for alteration, and ASAR data are useful for lineament mapping.展开更多
East Azarbaijan belongs to the Iranian plateau and is part of lesser Caucasus province. Studied area is located in west-central Alborz. The intrusion of oligocene bodies in various units causes the alteratio...East Azarbaijan belongs to the Iranian plateau and is part of lesser Caucasus province. Studied area is located in west-central Alborz. The intrusion of oligocene bodies in various units causes the alteration and mineralization in northwest of Iran. The Hizejan-Sharafabad is one of this named mineralized zone. Granitoidicrocks with component of Granodiorite to alkali have been influenced by hydrothermal fluids. Fractures and faults are as weak zone in earth surface and hydrothermal fluids rise to surface by these geological structures. These solutions cause to alteration in host rocks. Alteration zones are important features for the exploration of deposits. The altered rocks have specific absorption in some spectral portion and ASTER sensor is able to identify the type of alteration. Remote sensing method is useful tool for discovering altered area. The purpose of this study is to appraise ASTER data for surveying altered minerals in Hizejan-Sharafabad area in the event of detecting the potential mineralized areas. In this research, False Color Composite (FCC), Band ratio, and color composite ratio techniques are applied on ASTER data and Silica, Argilic, and Propylitic alteration zones are detected. These alteration types and mineralized area are related to Hizejan–Sharafabad fault which is absent in the fault maps. ASAR image processing has been used for lineaments and faults identified by the aid of Directional and Canny Algorithm filters. The structural study focuses on fracture zones and their characteristics including strike, length, and relationship with alteration zones.展开更多
The automatic all-sky imager developed by the Institute of Atmospheric Physics,Chinese Academy of Sciences,provides all-sky visible images in the red,green,and blue channels.This paper presents three major cali-bratio...The automatic all-sky imager developed by the Institute of Atmospheric Physics,Chinese Academy of Sciences,provides all-sky visible images in the red,green,and blue channels.This paper presents three major cali-bration experiments of the all-sky imager,geometric an-gular calibration,optical calibration,and radiometric calibration,and then infers an algorithm to retrieve rela-tive radiance from the all-sky images.Field experiments show that the related coefficient between retrieved radi-ance and measured radiance is about 0.91.It is feasible to use the algorithm to retrieve radiance from images.The paper sets up a relationship between radiance and the im-age,which is useful for using the all-sky image in nu-merical-simulations that predict more meteorological pa-rameters.展开更多
Quantitative and analytical analysis of the modulation process of the collimator is a great challenge,and is also of great value to the design and development of Fourier transform imaging telescopes.The Hard X-ray Ima...Quantitative and analytical analysis of the modulation process of the collimator is a great challenge,and is also of great value to the design and development of Fourier transform imaging telescopes.The Hard X-ray Imager(HXI),as one of the three payloads onboard the Advanced Space-based Solar Observatory(ASO-S) mission,adopts modulating Fourier-Transformation imaging technique and will be used to explore the mechanism of energy release and transmission in solar flare activities.As an important step to reconstruct the images of solar flares,accurate modulation functions of HXI are needed.In this paper,a mathematical model is developed to analyze the modulation function under a simplified condition first.Then its behavior under six degrees of freedom is calculated after adding the rotation matrix and translation change to the model.In addition,unparalleled light and extended sources are also considered so that our model can be used to analyze the X-ray beam experiment.Next,applied to the practical HXI conditions,the model has been confirmed not only by Geant4 simulations but also by some verification experiments.Furthermore,how this model helps to improve the image reconstruction process after the launch of ASO-S is also presented.展开更多
Calibration and characterization of focal plane hyperspectral imaging systems play an important role in natural scene imagery. Illumination plays a major role during imaging, as both the camera and electronically tuna...Calibration and characterization of focal plane hyperspectral imaging systems play an important role in natural scene imagery. Illumination plays a major role during imaging, as both the camera and electronically tunable filter may suffer low transmission at the ends of the visible spectrum, resulting in a low signal to noise ratio. It is important that the spectral characteristics of the imaging system as well as its geometric properties be well characterized and its radiometric performance known. The aim of this article is to identify the main sources of errors in a common design of focal-plane hyperspectral imaging system and devise ways of compensating for these errors. Calibration and characterization of a focal-plane hyperspectral imaging system include nominal wavelength accuracy analysis. This was carried out by capturing images of a mercury vapour lamp to study principal emission lines in the visible spectrum. The linearity of the hyperspectral imaging system was investigated by recording an input-output function. This was accomplished by comparing signals captured by the hyperspectral imaging system and luminance data recorded using a luminance meter. System noise characterization was done by repeated acquisitions of dark noise images captured under identical conditions. Main meridian analysis was accomplished by obtaining sample edge patches from the centre and near-boundary of hyperspectral image and then constructing edge and line spread functions. The final test image analysis involved verifying system calibration, image correction and compensation algorithms. Results show that with proper calibration and characterization of imaging systems, high quality images are obtained and can be used for research works which include hyperspectral image registration and hyperspectral image recognition for natural scenes.展开更多
基金supported by the Natural Science Foundation of Henan Province(Grant No.242300420297)awarded to Yi Sun.
文摘With the development of anti-virus technology,malicious documents have gradually become the main pathway of Advanced Persistent Threat(APT)attacks,therefore,the development of effective malicious document classifiers has become particularly urgent.Currently,detection methods based on document structure and behavioral features encounter challenges in feature engineering,these methods not only have limited accuracy,but also consume large resources,and usually can only detect documents in specific formats,which lacks versatility and adaptability.To address such problems,this paper proposes a novel malicious document detection method-visualizing documents as GGE images(Grayscale,Grayscale matrix,Entropy).The GGE method visualizes the original byte sequence of the malicious document as a grayscale image,the information entropy sequence of the document as an entropy image,and at the same time,the grayscale level co-occurrence matrix and the texture and spatial information stored in it are converted into grayscale matrix image,and fuses the three types of images to get the GGE color image.The Convolutional Block Attention Module-EfficientNet-B0(CBAM-EfficientNet-B0)model is then used for classification,combining transfer learning and applying the pre-trained model on the ImageNet dataset to the feature extraction process of GGE images.As shown in the experimental results,the GGE method has superior performance compared with other methods,which is suitable for detecting malicious documents in different formats,and achieves an accuracy of 99.44%and 97.39%on Portable Document Format(PDF)and office datasets,respectively,and consumes less time during the detection process,which can be effectively applied to the task of detecting malicious documents in real-time.
文摘BACKGROUND Focal nodular hyperplasia(FNH)-like lesions are hyperplastic formations in patients with micronodular cirrhosis and a history of alcohol abuse.Although pathologically similar to hepatocellular carcinoma(HCC)lesions,they are benign.As such,it is important to develop methods to distinguish between FNH-like lesions and HCC.AIM To evaluate diagnostically differential radiological findings between FNH-like lesions and HCC.METHODS We studied pathologically confirmed FNH-like lesions in 13 patients with alco-holic cirrhosis[10 men and 3 women;mean age:54.5±12.5(33-72)years]who were negative for hepatitis-B surface antigen and hepatitis-C virus antibody and underwent dynamic computed tomography(CT)and magnetic resonance imaging(MRI),including superparamagnetic iron oxide(SPIO)and/or gadoxetic acid-enhanced MRI.Seven patients also underwent angiography-assisted CT.RESULTS The evaluated lesion features included arterial enhancement pattern,washout appearance(low density compared with that of surrounding liver parenchyma),signal intensity on T1-weighted image(T1WI)and T2-weighted image(T2WI),central scar presence,chemical shift on in-and out-of-phase images,and uptake pattern on gadoxetic acid-enhanced MRI hepatobiliary phase and SPIO-enhanced MRI.Eleven patients had multiple small lesions(<1.5 cm).Radiological features of FNH-like lesions included hypervascularity despite small lesions,lack of“corona-like”enhancement in the late phase on CT during hepatic angiography(CTHA),high-intensity on T1WI,slightly high-or iso-intensity on T2WI,no signal decrease in out-of-phase images,and complete SPIO uptake or incomplete/partial uptake of gadoxetic acid.Pathologically,similar to HCC,FNH-like lesions showed many unpaired arteries and sinusoidal capillarization.CONCLUSION Overall,the present study showed that FNH-like lesions have unique radiological findings useful for differential diagnosis.Specifically,SPIO-and/or gadoxetic acid-enhanced MRI and CTHA features might facilitate differential diagnosis of FNH-like lesions and HCC.
文摘The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for identifying and mapping the quality of these herbal medicines.This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs.It focuses on feature extraction methods,image processing techniques,and the preparation of herbal images for compatibility with machine learning models.The article discusses commonly used image processing tools such as normalization,slicing,cropping,and augmentation to prepare images for artificial intelligence-based models.It also provides an overview of global herbal image databases and the models employed for herbal plant/drug identification.Readers will gain a comprehensive understanding of the potential application of various machine learning models,including artificial neural networks and convolutional neural networks.The article delves into suitable validation parameters like true positive rates,accuracy,precision,and more for the development of artificial intelligence-based identification and authentication techniques for herbal drugs.This article offers valuable insights and a conclusive platform for the further exploration of artificial intelligence in the field of herbal drugs,paving the way for smarter identification and authentication methods.
基金supported by Gansu Natural Science Foundation Programme(No.24JRRA231)National Natural Science Foundation of China(No.62061023)Gansu Provincial Education,Science and Technology Innovation and Industry(No.2021CYZC-04)。
文摘Brain tumor segmentation is critical in clinical diagnosis and treatment planning.Existing methods for brain tumor segmentation with missing modalities often struggle when dealing with multiple missing modalities,a common scenario in real-world clinical settings.These methods primarily focus on handling a single missing modality at a time,making them insufficiently robust for the additional complexity encountered with incomplete data containing various missing modality combinations.Additionally,most existing methods rely on single models,which may limit their performance and increase the risk of overfitting the training data.This work proposes a novel method called the ensemble adversarial co-training neural network(EACNet)for accurate brain tumor segmentation from multi-modal magnetic resonance imaging(MRI)scans with multiple missing modalities.The proposed method consists of three key modules:the ensemble of pre-trained models,which captures diverse feature representations from the MRI data by employing an ensemble of pre-trained models;adversarial learning,which leverages a competitive training approach involving two models;a generator model,which creates realistic missing data,while sub-networks acting as discriminators learn to distinguish real data from the generated“fake”data.Co-training framework utilizes the information extracted by the multimodal path(trained on complete scans)to guide the learning process in the path handling missing modalities.The model potentially compensates for missing information through co-training interactions by exploiting the relationships between available modalities and the tumor segmentation task.EACNet was evaluated on the BraTS2018 and BraTS2020 challenge datasets and achieved state-of-the-art and competitive performance respectively.Notably,the segmentation results for the whole tumor(WT)dice similarity coefficient(DSC)reached 89.27%,surpassing the performance of existing methods.The analysis suggests that the ensemble approach offers potential benefits,and the adversarial co-training contributes to the increased robustness and accuracy of EACNet for brain tumor segmentation of MRI scans with missing modalities.The experimental results show that EACNet has promising results for the task of brain tumor segmentation of MRI scans with missing modalities and is a better candidate for real-world clinical applications.
基金funded by the National Natural Science Foundation of China(Nos.22374055,22022404,22074050,82172055)the National Natural Science Foundation of Hubei Province(No.22022CFA033)the Fundamental Research Funds for the Central Universities(Nos.CCNU24JCPT001,CCNU24JCPT020)。
文摘Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for global food security and ecological balance.Currently,traditional detection strategies for monitoring plant health mainly rely on expensive equipment and complex operational procedures,which limit their widespread application.Fortunately,near-infrared(NIR)fluorescence and surface-enhanced Raman scattering(SERS)techniques have been recently highlighted in plants.NIR fluorescence imaging holds the advantages of being non-invasive,high-resolution and real-time,which is suitable for rapid screening in large-scale scenarios.While SERS enables highly sensitive and specific detection of trace chemical substances within plant tissues.Therefore,the complementarity of NIR fluorescence and SERS modalities can provide more comprehensive and accurate information for plant disease diagnosis and growth status monitoring.This article summarizes these two modalities in plant applications,and discusses the advantages of multimodal NIR fluorescence/SERS for a better understanding of a plant’s response to stress,thereby improving the accuracy and sensitivity of detection.
文摘Domain generalizable person re-identification(reid)is a challenging task in computer vision,which aims to apply a trained reid model to unseen domains.Prior works either combine the data in all the training domains to capture domain-invariant features,or adopt a mixture of experts to investigate domain-specific information.In this work,we argue that both domain-specific and domain-invariant features are crucial for improving the generalization ability of reid models.To this end,we design a novel framework,which we name two-stream adaptive learning(TAL),to simultaneously model these two kinds of information.Specifically,a domain-specific stream is proposed to capture the training domain statistics with batch normalization(BN)parameters,whereas an adaptive matching layer is designed to dynamically aggregate domain-level information.In the meantime,we design an adaptive BN layer in the domain-invariant stream to approximate the statistic of unseen domains,such that our model is capable of handling various novel scenes.These two streams work adaptively and collaboratively to learn generalizable reid features.As validated by extensive experiments,our framework can be applied to both single-source and multi-source domain generalization tasks,where the results show that our framework notably outperforms the state-of-the-art methods.
文摘Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing tools.The manual forgery localization is often reliant on forensic expertise.In recent times,machine learning(ML)and deep learning(DL)have shown promising results in automating image forgery localization.However,the ML-based method relies on hand-crafted features.Conversely,the DL method automatically extracts shallow spatial features to enhance the accuracy.However,DL-based methods lack the global co-relation of the features due to this performance degradation noticed in several applications.In the proposed study,we designed FLTNet(forgery localization transformer network)with a CNN(convolution neural network)encoder and transformer-based attention.The encoder extracts local high-dimensional features,and the transformer provides the global co-relation of the features.In the decoder,we have exclusively utilized a CNN to upsample the features that generate tampered mask images.Moreover,we evaluated visual and quantitative performance on three standard datasets and comparison with six state-of-the-art methods.The IoU values of the proposed method on CASIA V1,CASIA V2,and CoMoFoD datasets are 0.77,0.82,and 0.84,respectively.In addition,the F1-scores of these three datasets are 0.80,0.84,and 0.86,respectively.Furthermore,the visual results of the proposed method are clean and contain rich information,which can be used for real-time forgery detection.The code used in the study can be accessed through URL:https://github.com/ajit2k5/Forgery-Localization(accessed on 21 January 2025).
基金Supported by the National Natural Science Foundation of China(No.62001313)the Key Project of Liaoning Provincial Department of Science and Technology(No.2021JH2/10300134,2022JH1/10500004)。
文摘In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when faced with testing scenarios from unknown domains.To address this problem,this paper proposes a novel semi-supervised approach for cardiac magnetic resonance image segmentation,aiming to enhance predictive capabilities and domain generalization(DG).This paper establishes an MT-like model utilizing pseudo-labeling and consistency regularization from semi-supervised learning,and integrates uncertainty estimation to improve the accuracy of pseudo-labels.Additionally,to tackle the challenge of domain generalization,a data manipulation strategy is introduced,extracting spatial and content-related information from images across different domains,enriching the dataset with a multi-domain perspective.This papers method is meticulously evaluated on the publicly available cardiac magnetic resonance imaging dataset M&Ms,validating its effectiveness.Comparative analyses against various methods highlight the out-standing performance of this papers approach,demonstrating its capability to segment cardiac magnetic resonance images in previously unseen domains even with limited annotated data.
基金supported by the Medium and Long-term Science and Technology Plan for Radio,Television,and Online Audiovisuals(2023AC0200)the Public Welfare Technology Application Research Project of Zhejiang Province,China(No.LGF21F010001).
文摘Recent deep neural network(DNN)based blind image quality assessment(BIQA)approaches take mean opinion score(MOS)as ground-truth labels,which would lead to cross-datasets biases and limited generalization ability of the DNN-based BIQA model.This work validates the natural instability of MOS through investigating the neuropsychological characteristics inside the human visual system during quality perception.By combining persistent homology analysis with electroencephalogram(EEG),the physiologically meaningful features of the brain responses to different distortion levels are extracted.The physiological features indicate that although volunteers view exactly the same image content,their EEG features are quite varied.Based on the physiological results,we advocate treating MOS as noisy labels and optimizing the DNN based BIQA model with earlystop strategies.Experimental results on both innerdataset and cross-dataset demonstrate the superiority of our optimization approach in terms of generalization ability.
基金This work was supported by the Russian Science Foundation(Grant No.22-19-00765)at the Perm National Research Polytechnic University.
文摘Creating conditions to implement equilibrium processes of damage accumulation under a predictable scenario enables control over the failure of structural elements in critical states.It improves safety and reduces the probability of catastrophic behavior in case of accidents.Equilibrium damage accumulation in some cases leads to a falling part(called a postcritical stage)on the material’s stress-strain curve.It must be taken into account to assess the strength and deformation limits of composite structures.Digital image correlation method,acoustic emission(AE)signals recording,and optical microscopy were used in this paper to study the deformation and failure processes of an orthogonal-layup composite during tension in various directions to orthotropy axes.An elastic-plastic deformation model was proposed for the composite in a plane stress condition.The evolution of strain fields and neck formation were analyzed.The staging of the postcritical deformation process was described.AE signals obtained during tests were studied;characteristic damage types of a material were defined.The rationality and necessity of polymer composites’postcritical deformation stage taken into account in refined strength analysis of structures were concluded.
基金Supported by Hubei Provincial Department of Science and Technology Natural Fund,No.2024AFC056the Open Fund of the Mental Health Research Institute at Three Gorges University,No.YCXL-23-11.
文摘BACKGROUND Major depressive disorder(MDD)with comorbid anxiety is an intricate psychiatric condition,but limited research is available on the degree centrality(DC)between anxious MDD and nonanxious MDD patients.AIM To examine changes in DC values and their use as neuroimaging biomarkers in anxious and non-anxious MDD patients.METHODS We examined 23 anxious MDD patients,30 nonanxious MDD patients,and 28 healthy controls(HCs)using the DC for data analysis.RESULTS Compared with HCs,the anxious MDD group reported markedly reduced DC values in the right fusiform gyrus(FFG)and inferior occipital gyrus,whereas elevated DC values in the left middle frontal gyrus and left inferior parietal angular gyrus.The nonanxious MDD group exhibited surged DC values in the bilateral cerebellum IX,right precuneus,and opercular part of the inferior frontal gyrus.Unlike the nonanxious MDD group,the anxious MDD group exhibited declined DC values in the right FFG and bilateral calcarine(CAL).Besides,declined DC values in the right FFG and bilateral CAL negatively correlated with anxiety scores in the MDD group.CONCLUSION This study shows that abnormal DC patterns in MDD,especially in the left CAL,can distinguish MDD from its anxiety subtype,indicating a potential neuroimaging biomarker.
文摘In this paper,through the use of image space analysis,optimality conditions for a class of variational inequalities with cone constraints are proposed.By virtue of the nonlinear scalarization function,known as the Gerstewitz function,three nonlinear weak separation functions,two nonlinear regular weak separation functions and a nonlinear strong separation function are introduced.According to nonlinearseparation functions,some optimality conditions of the weak and strong alternative for variational inequalities with cone constraints are derived.
文摘Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective intervention.While Deep Learning(DL)approaches have shown promise in AD diagnosis,existing methods often struggle with the issues of precision,interpretability,and class imbalance.This study presents a novel framework that integrates DL with several eXplainable Artificial Intelligence(XAI)techniques,in particular attention mechanisms,Gradient-Weighted Class Activation Mapping(Grad-CAM),and Local Interpretable Model-Agnostic Explanations(LIME),to improve bothmodel interpretability and feature selection.The study evaluates four different DL architectures(ResMLP,VGG16,Xception,and Convolutional Neural Network(CNN)with attention mechanism)on a balanced dataset of 3714 MRI brain scans from patients aged 70 and older.The proposed CNN with attention model achieved superior performance,demonstrating 99.18%accuracy on the primary dataset and 96.64% accuracy on the ADNI dataset,significantly advancing the state-of-the-art in AD classification.The ability of the framework to provide comprehensive,interpretable results through multiple visualization techniques while maintaining high classification accuracy represents a significant advancement in the computational diagnosis of AD,potentially enabling more accurate and earlier intervention in clinical settings.
基金This paper is supported by the National Natural Science Foundation ofChina (No .40371107) .
文摘In order to evaluate radiometric normalization techniques, two image normalization algorithms for absolute radiometric correction of Landsat imagery were quantitatively compared in this paper, which are the Illumination Correction Model proposed by Markham and Irish and the Illumination and Atmospheric Correction Model developed by the Remote Sensing and GIS Laboratory of the Utah State University. Relative noise, correlation coefficient and slope value were used as the criteria for the evaluation and comparison, which were derived from pseudo-invarlant features identified from multitemporal Landsat image pairs of Xiamen （厦门） and Fuzhou （福州） areas, both located in the eastern Fujian （福建） Province of China. Compared with the unnormalized image, the radiometric differences between the normalized multitemporal images were significantly reduced when the seasons of multitemporal images were different. However, there was no significant difference between the normalized and unnorrealized images with a similar seasonal condition. Furthermore, the correction results of two algorithms are similar when the images are relatively clear with a uniform atmospheric condition. Therefore, the radiometric normalization procedures should be carried out if the multitemporal images have a significant seasonal difference.
文摘The NW part of Iran belongs to the Iranian plateau that is a tectonically active region within the Alpine-Himalayan orogenic belt. The intrusion of Oligocene parts in various faces caused the alteration and mineralization such as copper, molybdenum, gold and iron in the Siyahrood area. Granitoidic rocks with component of Granodiorite to alkali have been influenced by hydrothermal fluids. Alteration zones are important features for the exploration of deposits and the ASTER sensor is able to identify the type of alteration and its alteration zoning. This method can be a useful tool for detecting potential mineralization area in East Azarbaijan—Northwest of Iran. The purpose of this study is to evaluate ASTER data for mapping altered minerals in Siyahrood area in order to detect the potential mineralized areas. In this study, false color composite, and band ratio techniques were applied on ASTER data and argillic, phyllic, Iron oxide and propylitic alteration zones were separated. ASAR image processing has been used for lineaments and faults identified by the aid of directional filter. The structural study focused on fracture zones and their characteristics including strike, length, and relationship with alteration zones. The results of this study demonstrate the usefulness of remote sensing methods and ASTER multi-spectral data for alteration, and ASAR data are useful for lineament mapping.
文摘East Azarbaijan belongs to the Iranian plateau and is part of lesser Caucasus province. Studied area is located in west-central Alborz. The intrusion of oligocene bodies in various units causes the alteration and mineralization in northwest of Iran. The Hizejan-Sharafabad is one of this named mineralized zone. Granitoidicrocks with component of Granodiorite to alkali have been influenced by hydrothermal fluids. Fractures and faults are as weak zone in earth surface and hydrothermal fluids rise to surface by these geological structures. These solutions cause to alteration in host rocks. Alteration zones are important features for the exploration of deposits. The altered rocks have specific absorption in some spectral portion and ASTER sensor is able to identify the type of alteration. Remote sensing method is useful tool for discovering altered area. The purpose of this study is to appraise ASTER data for surveying altered minerals in Hizejan-Sharafabad area in the event of detecting the potential mineralized areas. In this research, False Color Composite (FCC), Band ratio, and color composite ratio techniques are applied on ASTER data and Silica, Argilic, and Propylitic alteration zones are detected. These alteration types and mineralized area are related to Hizejan–Sharafabad fault which is absent in the fault maps. ASAR image processing has been used for lineaments and faults identified by the aid of Directional and Canny Algorithm filters. The structural study focuses on fracture zones and their characteristics including strike, length, and relationship with alteration zones.
基金supported by the national natural science foundation of China (Grant Nos.40505006 and 40775026)
文摘The automatic all-sky imager developed by the Institute of Atmospheric Physics,Chinese Academy of Sciences,provides all-sky visible images in the red,green,and blue channels.This paper presents three major cali-bration experiments of the all-sky imager,geometric an-gular calibration,optical calibration,and radiometric calibration,and then infers an algorithm to retrieve rela-tive radiance from the all-sky images.Field experiments show that the related coefficient between retrieved radi-ance and measured radiance is about 0.91.It is feasible to use the algorithm to retrieve radiance from images.The paper sets up a relationship between radiance and the im-age,which is useful for using the all-sky image in nu-merical-simulations that predict more meteorological pa-rameters.
基金supported by the Strategic Priority Research Program on Space ScienceChinese Academy of Sciences(No.XDA 15320104)+2 种基金the Scientific Instrument Developing Project of the Chinese Academy of Sciences(Grant No.YJKYYQ20200077)the National Natural Science Foundation of China(Nos.12173100,12022302,11803093 and 11973097)the Youth Innovation Promotion Association,CAS(No.2021317 and Y2021087)。
文摘Quantitative and analytical analysis of the modulation process of the collimator is a great challenge,and is also of great value to the design and development of Fourier transform imaging telescopes.The Hard X-ray Imager(HXI),as one of the three payloads onboard the Advanced Space-based Solar Observatory(ASO-S) mission,adopts modulating Fourier-Transformation imaging technique and will be used to explore the mechanism of energy release and transmission in solar flare activities.As an important step to reconstruct the images of solar flares,accurate modulation functions of HXI are needed.In this paper,a mathematical model is developed to analyze the modulation function under a simplified condition first.Then its behavior under six degrees of freedom is calculated after adding the rotation matrix and translation change to the model.In addition,unparalleled light and extended sources are also considered so that our model can be used to analyze the X-ray beam experiment.Next,applied to the practical HXI conditions,the model has been confirmed not only by Geant4 simulations but also by some verification experiments.Furthermore,how this model helps to improve the image reconstruction process after the launch of ASO-S is also presented.
文摘Calibration and characterization of focal plane hyperspectral imaging systems play an important role in natural scene imagery. Illumination plays a major role during imaging, as both the camera and electronically tunable filter may suffer low transmission at the ends of the visible spectrum, resulting in a low signal to noise ratio. It is important that the spectral characteristics of the imaging system as well as its geometric properties be well characterized and its radiometric performance known. The aim of this article is to identify the main sources of errors in a common design of focal-plane hyperspectral imaging system and devise ways of compensating for these errors. Calibration and characterization of a focal-plane hyperspectral imaging system include nominal wavelength accuracy analysis. This was carried out by capturing images of a mercury vapour lamp to study principal emission lines in the visible spectrum. The linearity of the hyperspectral imaging system was investigated by recording an input-output function. This was accomplished by comparing signals captured by the hyperspectral imaging system and luminance data recorded using a luminance meter. System noise characterization was done by repeated acquisitions of dark noise images captured under identical conditions. Main meridian analysis was accomplished by obtaining sample edge patches from the centre and near-boundary of hyperspectral image and then constructing edge and line spread functions. The final test image analysis involved verifying system calibration, image correction and compensation algorithms. Results show that with proper calibration and characterization of imaging systems, high quality images are obtained and can be used for research works which include hyperspectral image registration and hyperspectral image recognition for natural scenes.
