On a global scale,lung cancer is responsible for around 27%of all cancer fatalities.Even though there have been great strides in diagnosis and therapy in recent years,the five-year cure rate is just 19%.Classification...On a global scale,lung cancer is responsible for around 27%of all cancer fatalities.Even though there have been great strides in diagnosis and therapy in recent years,the five-year cure rate is just 19%.Classification is crucial for diagnosing lung nodules.This is especially true today that automated categorization may provide a professional opinion that can be used by doctors.New computer vision and machine learning techniques have made possible accurate and quick categorization of CT images.This field of research has exploded in popularity in recent years because of its high efficiency and ability to decrease labour requirements.Here,they want to look carefully at the current state of automated categorization of lung nodules.Generalpurpose structures are briefly discussed,and typical algorithms are described.Our results show deep learning-based lung nodule categorization quickly becomes the industry standard.Therefore,it is critical to pay greater attention to the coherence of the data inside the study and the consistency of the research topic.Furthermore,there should be greater collaboration between designers,medical experts,and others in the field.展开更多
In healthcare sector,image classification is one of the crucial problems that impact the quality output from image processing domain.The purpose of image classification is to categorize different healthcare images under...In healthcare sector,image classification is one of the crucial problems that impact the quality output from image processing domain.The purpose of image classification is to categorize different healthcare images under various class labels which in turn helps in the detection and management of diseases.Magnetic Resonance Imaging(MRI)is one of the effective non-invasive strate-gies that generate a huge and distinct number of tissue contrasts in every imaging modality.This technique is commonly utilized by healthcare professionals for Brain Tumor(BT)diagnosis.With recent advancements in Machine Learning(ML)and Deep Learning(DL)models,it is possible to detect the tumor from images automatically,using a computer-aided design.The current study focuses on the design of automated Deep Learning-based BT Detection and Classification model using MRI images(DLBTDC-MRI).The proposed DLBTDC-MRI techni-que aims at detecting and classifying different stages of BT.The proposed DLBTDC-MRI technique involves medianfiltering technique to remove the noise and enhance the quality of MRI images.Besides,morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI image.Moreover,a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature vectors.Finally,Artificial Fish Swarm Optimization(AFSO)with Artificial Neural Network(ANN)model is utilized as a classifier to decide the presence of BT.In order to assess the enhanced BT classification performance of the proposed model,a comprehensive set of simulations was performed on benchmark dataset and the results were vali-dated under several measures.展开更多
Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer a...Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist radiologists.With the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided design.This study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer classification.The proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain tumors.The proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of MRI images.In addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the MRI image.Moreover,a fusion of handcrafted with deep features using Residual Network(ResNet)is utilized as feature extractors.Finally,HGSO algorithm with kernel extreme learning machine(KELM)model was utilized for identifying the presence of brain tumors.For examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BRATS 2015 dataset.展开更多
Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learni...Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learning have significantly enhanced road scene classification,simultaneously achieving high accuracy,computational efficiency,and adaptability across diverse conditions continues to be difficult.To address these challenges,this study proposes HybridLSTM,a novel and efficient framework that integrates deep learning-based,object-based,and handcrafted feature extraction methods within a unified architecture.HybridLSTM is designed to classify four distinct road scene categories—crosswalk(CW),highway(HW),overpass/tunnel(OP/T),and parking(P)—by leveraging multiple publicly available datasets,including Places-365,BDD100K,LabelMe,and KITTI,thereby promoting domain generalization.The framework fuses object-level features extracted using YOLOv5 and VGG19,scene-level global representations obtained from a modified VGG19,and fine-grained texture features captured through eight handcrafted descriptors.This hybrid feature fusion enables the model to capture both semantic context and low-level visual cues,which are critical for robust scene understanding.To model spatial arrangements and latent sequential dependencies present even in static imagery,the combined features are processed through a Long Short-Term Memory(LSTM)network,allowing the extraction of discriminative patterns across heterogeneous feature spaces.Extensive experiments conducted on 2725 annotated road scene images,with an 80:20 training-to-testing split,validate the effectiveness of the proposed model.HybridLSTM achieves a classification accuracy of 96.3%,a precision of 95.8%,a recall of 96.1%,and an F1-score of 96.0%,outperforming several existing state-of-the-art methods.These results demonstrate the robustness,scalability,and generalization capability of HybridLSTM across varying environments and scene complexities.Moreover,the framework is optimized to balance classification performance with computational efficiency,making it highly suitable for real-time deployment in embedded autonomous driving systems.Future work will focus on extending the model to multi-class detection within a single frame and optimizing it further for edge-device deployments to reduce computational overhead in practical applications.展开更多
Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining th...Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining them.The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge.At the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data representation.With this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)model.The proposed EHCDLHIC technique initially performs Weiner filtering based noise removal technique.Once the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are extracted.For the classification process,the bidirectional gated recurrent unit(BGRU)model can be employed.At the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the work.For validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is performed.The experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.展开更多
Automatic gastrointestinal(GI)tract disease recognition is an important application of biomedical image processing.Conventionally,microscopic analysis of pathological tissue is used to detect abnormal areas of the GI ...Automatic gastrointestinal(GI)tract disease recognition is an important application of biomedical image processing.Conventionally,microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract.The procedure is subjective and results in significant inter-/intraobserver variations in disease detection.Moreover,a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination.Consequently,there is a huge demand for a reliable computer-aided diagnostic system(CADx)for diagnosing GI tract diseases.In this work,a CADx was proposed for the diagnosis and classification of GI tract diseases.A novel framework is presented where preprocessing(LAB color space)is performed first;then local binary patterns(LBP)or texture and deep learning(inceptionNet,ResNet50,and VGG-16)features are fused serially to improve the prediction of the abnormalities in the GI tract.Additionally,principal component analysis(PCA),entropy,and minimum redundancy and maximum relevance(mRMR)feature selection methods were analyzed to acquire the optimized characteristics,and various classifiers were trained using the fused features.Open-source color image datasets(KVASIR,NERTHUS,and stomach ULCER)were used for performance evaluation.The study revealed that the subspace discriminant classifier provided an efficient result with 95.02%accuracy on the KVASIR dataset,which proved to be better than the existing state-of-the-art approaches.展开更多
Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or select...Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.展开更多
Aim:Lung cancer remains a major global health challenge,and this study presents a censor-aware semi-supervised learning framework(SSL)that integrates clinical and imaging data to improve prognostic modeling and addres...Aim:Lung cancer remains a major global health challenge,and this study presents a censor-aware semi-supervised learning framework(SSL)that integrates clinical and imaging data to improve prognostic modeling and address biases in handling censored data.Methods:We analyzed clinical,positron emission tomography(PET),and computed tomography(CT)data from 199 lung cancer patients from public and local databases,focusing on overall survival time as the primary outcome.Handcrafted(HRF)and Deep Radiomics features were extracted after preprocessing using Visualized&Standardized Environment for Radiomics Analysis(ViSERA)software and were combined with clinical features.Features were reduced using Pearson’s correlation coefficient regression(RR)and the F-test for regression(FR),followed by supervised learning(SL)and SSL.In SSL,censored data were pseudo-labeled using the Weibull accelerated failure time(AFT)model to enrich the training data.Seven regressors and three hazard ratio survival analyses(HRSAs)were optimized using five-fold cross-validation,grid search,and holdout test bootstrapping.Results:For PET-HRFs,the SSL approach reduced the mean absolute error by 14.81%,achieving 1.04 years with FR+AdaBoost Regression(ABR)compared to 1.20 years with SL.For clinical features,SSL with RR+ABR reached a mean absolute error of 1.04 years,outperforming SL(1.09 years)with a 4.9%improvement.In HRSA,CT_HRF combined with principal component analysis(PCA)+Component-Wise Gradient Boosting Survival Analysis yielded an external C-index of 0.65±0.02,effectively distinguishing high-and low-risk groups.Conclusions:The SSL strategy applied to HRFs from PET imaging significantly enhanced survival prediction compared to SL and uncovered complementary biological information that may remain hidden when only limited labeled data are used.展开更多
Three-dimensional skeleton-based action recognition(3D SAR)has gained important attention within the computer vision community,owing to the inherent advantages offered by skeleton data.As a result,a plethora of impres...Three-dimensional skeleton-based action recognition(3D SAR)has gained important attention within the computer vision community,owing to the inherent advantages offered by skeleton data.As a result,a plethora of impressive works,including those based on conventional handcrafted features and learned feature extraction methods,have been conducted over the years.However,prior surveys on action recognition have primarily focused on video or red-green-blue(RGB)data-dominated approaches,with limited coverage of reviews related to skeleton data.Furthermore,despite the extensive application of deep learning methods in this field,there has been a notable absence of research that provides an introductory or comprehensive review from the perspective of deep learning architectures.To address these limitations,this survey first underscores the importance of action recognition and emphasizes the significance of 3-dimensional(3D)skeleton data as a valuable modality.Subsequently,we provide a comprehensive introduction to mainstream action recognition techniques based on 4 fundamental deep architectures,i.e.,recurrent neural networks,convolutional neural networks,graph convolutional network,and Transformers.All methods with the corresponding architectures are then presented in a data-driven manner with detailed discussion.Finally,we offer insights into the current largest 3D skeleton dataset,NTU-RGB+D,and its new edition,NTU-RGB+D 120,along with an overview of several top-performing algorithms on these datasets.To the best of our knowledge,this research represents the first comprehensive discussion of deep learning-based action recognition using 3D skeleton data.展开更多
文摘On a global scale,lung cancer is responsible for around 27%of all cancer fatalities.Even though there have been great strides in diagnosis and therapy in recent years,the five-year cure rate is just 19%.Classification is crucial for diagnosing lung nodules.This is especially true today that automated categorization may provide a professional opinion that can be used by doctors.New computer vision and machine learning techniques have made possible accurate and quick categorization of CT images.This field of research has exploded in popularity in recent years because of its high efficiency and ability to decrease labour requirements.Here,they want to look carefully at the current state of automated categorization of lung nodules.Generalpurpose structures are briefly discussed,and typical algorithms are described.Our results show deep learning-based lung nodule categorization quickly becomes the industry standard.Therefore,it is critical to pay greater attention to the coherence of the data inside the study and the consistency of the research topic.Furthermore,there should be greater collaboration between designers,medical experts,and others in the field.
基金supported through the Annual Funding track by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Project No.AN000684].
文摘In healthcare sector,image classification is one of the crucial problems that impact the quality output from image processing domain.The purpose of image classification is to categorize different healthcare images under various class labels which in turn helps in the detection and management of diseases.Magnetic Resonance Imaging(MRI)is one of the effective non-invasive strate-gies that generate a huge and distinct number of tissue contrasts in every imaging modality.This technique is commonly utilized by healthcare professionals for Brain Tumor(BT)diagnosis.With recent advancements in Machine Learning(ML)and Deep Learning(DL)models,it is possible to detect the tumor from images automatically,using a computer-aided design.The current study focuses on the design of automated Deep Learning-based BT Detection and Classification model using MRI images(DLBTDC-MRI).The proposed DLBTDC-MRI techni-que aims at detecting and classifying different stages of BT.The proposed DLBTDC-MRI technique involves medianfiltering technique to remove the noise and enhance the quality of MRI images.Besides,morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI image.Moreover,a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature vectors.Finally,Artificial Fish Swarm Optimization(AFSO)with Artificial Neural Network(ANN)model is utilized as a classifier to decide the presence of BT.In order to assess the enhanced BT classification performance of the proposed model,a comprehensive set of simulations was performed on benchmark dataset and the results were vali-dated under several measures.
基金This research work was funded by Institutional fund projects under grant no.(IFPHI-180-612-2020)Therefore,the authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist radiologists.With the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided design.This study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer classification.The proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain tumors.The proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of MRI images.In addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the MRI image.Moreover,a fusion of handcrafted with deep features using Residual Network(ResNet)is utilized as feature extractors.Finally,HGSO algorithm with kernel extreme learning machine(KELM)model was utilized for identifying the presence of brain tumors.For examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BRATS 2015 dataset.
文摘Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learning have significantly enhanced road scene classification,simultaneously achieving high accuracy,computational efficiency,and adaptability across diverse conditions continues to be difficult.To address these challenges,this study proposes HybridLSTM,a novel and efficient framework that integrates deep learning-based,object-based,and handcrafted feature extraction methods within a unified architecture.HybridLSTM is designed to classify four distinct road scene categories—crosswalk(CW),highway(HW),overpass/tunnel(OP/T),and parking(P)—by leveraging multiple publicly available datasets,including Places-365,BDD100K,LabelMe,and KITTI,thereby promoting domain generalization.The framework fuses object-level features extracted using YOLOv5 and VGG19,scene-level global representations obtained from a modified VGG19,and fine-grained texture features captured through eight handcrafted descriptors.This hybrid feature fusion enables the model to capture both semantic context and low-level visual cues,which are critical for robust scene understanding.To model spatial arrangements and latent sequential dependencies present even in static imagery,the combined features are processed through a Long Short-Term Memory(LSTM)network,allowing the extraction of discriminative patterns across heterogeneous feature spaces.Extensive experiments conducted on 2725 annotated road scene images,with an 80:20 training-to-testing split,validate the effectiveness of the proposed model.HybridLSTM achieves a classification accuracy of 96.3%,a precision of 95.8%,a recall of 96.1%,and an F1-score of 96.0%,outperforming several existing state-of-the-art methods.These results demonstrate the robustness,scalability,and generalization capability of HybridLSTM across varying environments and scene complexities.Moreover,the framework is optimized to balance classification performance with computational efficiency,making it highly suitable for real-time deployment in embedded autonomous driving systems.Future work will focus on extending the model to multi-class detection within a single frame and optimizing it further for edge-device deployments to reduce computational overhead in practical applications.
文摘Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining them.The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge.At the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data representation.With this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)model.The proposed EHCDLHIC technique initially performs Weiner filtering based noise removal technique.Once the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are extracted.For the classification process,the bidirectional gated recurrent unit(BGRU)model can be employed.At the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the work.For validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is performed.The experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.
基金This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘Automatic gastrointestinal(GI)tract disease recognition is an important application of biomedical image processing.Conventionally,microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract.The procedure is subjective and results in significant inter-/intraobserver variations in disease detection.Moreover,a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination.Consequently,there is a huge demand for a reliable computer-aided diagnostic system(CADx)for diagnosing GI tract diseases.In this work,a CADx was proposed for the diagnosis and classification of GI tract diseases.A novel framework is presented where preprocessing(LAB color space)is performed first;then local binary patterns(LBP)or texture and deep learning(inceptionNet,ResNet50,and VGG-16)features are fused serially to improve the prediction of the abnormalities in the GI tract.Additionally,principal component analysis(PCA),entropy,and minimum redundancy and maximum relevance(mRMR)feature selection methods were analyzed to acquire the optimized characteristics,and various classifiers were trained using the fused features.Open-source color image datasets(KVASIR,NERTHUS,and stomach ULCER)were used for performance evaluation.The study revealed that the subspace discriminant classifier provided an efficient result with 95.02%accuracy on the KVASIR dataset,which proved to be better than the existing state-of-the-art approaches.
基金supported by the National Natural Science Foundation of China (67441830108 and 41871224)。
文摘Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.
基金supported by the UBC Department of Radiology 2023 AI Fund,and the Natural Sciences and Engineering Research Council of Canada(NSERC)Discovery Horizons Grant(DH-2025-00119).
文摘Aim:Lung cancer remains a major global health challenge,and this study presents a censor-aware semi-supervised learning framework(SSL)that integrates clinical and imaging data to improve prognostic modeling and address biases in handling censored data.Methods:We analyzed clinical,positron emission tomography(PET),and computed tomography(CT)data from 199 lung cancer patients from public and local databases,focusing on overall survival time as the primary outcome.Handcrafted(HRF)and Deep Radiomics features were extracted after preprocessing using Visualized&Standardized Environment for Radiomics Analysis(ViSERA)software and were combined with clinical features.Features were reduced using Pearson’s correlation coefficient regression(RR)and the F-test for regression(FR),followed by supervised learning(SL)and SSL.In SSL,censored data were pseudo-labeled using the Weibull accelerated failure time(AFT)model to enrich the training data.Seven regressors and three hazard ratio survival analyses(HRSAs)were optimized using five-fold cross-validation,grid search,and holdout test bootstrapping.Results:For PET-HRFs,the SSL approach reduced the mean absolute error by 14.81%,achieving 1.04 years with FR+AdaBoost Regression(ABR)compared to 1.20 years with SL.For clinical features,SSL with RR+ABR reached a mean absolute error of 1.04 years,outperforming SL(1.09 years)with a 4.9%improvement.In HRSA,CT_HRF combined with principal component analysis(PCA)+Component-Wise Gradient Boosting Survival Analysis yielded an external C-index of 0.65±0.02,effectively distinguishing high-and low-risk groups.Conclusions:The SSL strategy applied to HRFs from PET imaging significantly enhanced survival prediction compared to SL and uncovered complementary biological information that may remain hidden when only limited labeled data are used.
基金supported by the National Natural Science Foundation of China(No.62203476)the Natural Science Foundation of Shenzhen(No.JCYJ20230807120801002).
文摘Three-dimensional skeleton-based action recognition(3D SAR)has gained important attention within the computer vision community,owing to the inherent advantages offered by skeleton data.As a result,a plethora of impressive works,including those based on conventional handcrafted features and learned feature extraction methods,have been conducted over the years.However,prior surveys on action recognition have primarily focused on video or red-green-blue(RGB)data-dominated approaches,with limited coverage of reviews related to skeleton data.Furthermore,despite the extensive application of deep learning methods in this field,there has been a notable absence of research that provides an introductory or comprehensive review from the perspective of deep learning architectures.To address these limitations,this survey first underscores the importance of action recognition and emphasizes the significance of 3-dimensional(3D)skeleton data as a valuable modality.Subsequently,we provide a comprehensive introduction to mainstream action recognition techniques based on 4 fundamental deep architectures,i.e.,recurrent neural networks,convolutional neural networks,graph convolutional network,and Transformers.All methods with the corresponding architectures are then presented in a data-driven manner with detailed discussion.Finally,we offer insights into the current largest 3D skeleton dataset,NTU-RGB+D,and its new edition,NTU-RGB+D 120,along with an overview of several top-performing algorithms on these datasets.To the best of our knowledge,this research represents the first comprehensive discussion of deep learning-based action recognition using 3D skeleton data.
