Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Aug...Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation(BSDA)with a Vision Mamba-based model for medical image classification(MedMamba),enhanced by residual connection blocks,we named the model BSDA-Mamba.BSDA augments medical image data semantically,enhancing the model’s generalization ability and classification performance.MedMamba,a deep learning-based state space model,excels in capturing long-range dependencies in medical images.By incorporating residual connections,BSDA-Mamba further improves feature extraction capabilities.Through comprehensive experiments on eight medical image datasets,we demonstrate that BSDA-Mamba outperforms existing models in accuracy,area under the curve,and F1-score.Our results highlight BSDA-Mamba’s potential as a reliable tool for medical image analysis,particularly in handling diverse imaging modalities from X-rays to MRI.The open-sourcing of our model’s code and datasets,will facilitate the reproduction and extension of our work.展开更多
3D model classification has emerged as a significant research focus in computer vision.However,traditional convolutional neural networks(CNNs)often struggle to capture global dependencies across both height and width ...3D model classification has emerged as a significant research focus in computer vision.However,traditional convolutional neural networks(CNNs)often struggle to capture global dependencies across both height and width dimensions simultaneously,leading to limited feature representation capabilities when handling complex visual tasks.To address this challenge,we propose a novel 3D model classification network named ViT-GE(Vision Transformer with Global and Efficient Attention),which integrates Global Grouped Coordinate Attention(GGCA)and Efficient Channel Attention(ECA)mechanisms.Specifically,the Vision Transformer(ViT)is employed to extract comprehensive global features from multi-view inputs using its self-attention mechanism,effectively capturing 3D shape characteristics.To further enhance spatial feature modeling,the GGCA module introduces a grouping strategy and global context interactions.Concurrently,the ECA module strengthens inter-channel information flow,enabling the network to adaptively emphasize key features and improve feature fusion.Finally,a voting mechanism is adopted to enhance classification accuracy,robustness,and stability.Experimental results on the ModelNet10 dataset demonstrate that our method achieves a classification accuracy of 93.50%,validating its effectiveness and superior performance.展开更多
The theoretical framework of visual simulation in virtual reality is discussed. The new concept of visual image space is supposed. On the basis of visual image space, in visual perceptive sense, VR is considered as a ...The theoretical framework of visual simulation in virtual reality is discussed. The new concept of visual image space is supposed. On the basis of visual image space, in visual perceptive sense, VR is considered as a spatial simulation. The objective of the spatial simulation is to transform physical space to visual image space. Last, the prototype system, surveying & mapping virtual Reality (SMVR), is developed, and the space simulation above is realized. By use of SMVR, the real 3D representation, 3D visual analysis, virtual plan and designs can be implemented.展开更多
Purpose： We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes： First, to demonstrate the use of a science mapping approach to perform the review ...Purpose： We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes： First, to demonstrate the use of a science mapping approach to perform the review so that researchers may apply the procedure to the review of a scientific domain of their own interest, and second, to identify major areas of research activities concerning science mapping, intellectual milestones in the development of key specialties, evolutionary stages of major specialties involved, and the dynamics of transitions from one specialty to another.Design/methodology/approach： We first introduce a theoretical framework of the evolution of a scientific specialty. Then we demonstrate a generic search strategy that can be used to construct a representative dataset of bibliographic records of a domain of research. Next, progressively synthesized co-citation networks are constructed and visualized to aid visual analytic studies of the domain＇s structural and dynamic patterns and trends. Finally, trajectories of citations made by particular types of authors and articles are presented to illustrate the predictive potential of the analytic approach.Findings： The evolution of the science mapping research involves the development of a number of interrelated specialties. Four major specialties are discussed in detail in terms of four evolutionary stages： conceptualization, tool construction, application, and codification. Underlying connections between major specialties are also explored. The predictive analysis demonstrates citations trajectories of potentially transformative contributions.Research limitations： The systematic review is primarily guided by citation patterns in the dataset retrieved from the literature. The scope of the data is limited by the source of the retrieval, i.e. the Web of Science, and the composite query used. An iterative query refinement is possible if one would like to improve the data quality, although the current approach serves our purpose adequately. More in-depth analyses of each specialty would be more revealing by incorporating additional methods such as citation context analysis and studies of other aspects of scholarly publications.Practical implications： The underlying analytic process of science mapping serves many practical needs, notably bibliometric mapping, knowledge domain visualization, and visualization of scientific literature. In order to master such a complex process of science mapping, researchers often need to develop a diverse set of skills and knowledge that may span multiple disciplines. The approach demonstrated in this article provides a generic method for conducting a systematic review.Originality/value： Incorporating the evolutionary stages of a specialty into the visual analytic study of a research domain is innovative. It provides a systematic methodology for researchers to achieve a good understanding of how scientific fields evolve, to recognize potentially insightful patterns from visually encoded signs, and to synthesize various information so as to capture the state of the art of the domain.展开更多
Purified terephthalic acid(PTA) is an important chemical raw material. P-xylene(PX) is transformed to terephthalic acid(TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a co...Purified terephthalic acid(PTA) is an important chemical raw material. P-xylene(PX) is transformed to terephthalic acid(TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a complex process involving three-phase reaction of gas, liquid and solid. To monitor the process and to improve the product quality, as well as to visualize the fault type clearly, a fault diagnosis method based on selforganizing map(SOM) and high dimensional feature extraction method, local tangent space alignment(LTSA),is proposed. In this method, LTSA can reduce the dimension and keep the topology information simultaneously,and SOM distinguishes various states on the output map. Monitoring results of PX oxidation reaction process indicate that the LTSA–SOM can well detect and visualize the fault type.展开更多
文摘Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation(BSDA)with a Vision Mamba-based model for medical image classification(MedMamba),enhanced by residual connection blocks,we named the model BSDA-Mamba.BSDA augments medical image data semantically,enhancing the model’s generalization ability and classification performance.MedMamba,a deep learning-based state space model,excels in capturing long-range dependencies in medical images.By incorporating residual connections,BSDA-Mamba further improves feature extraction capabilities.Through comprehensive experiments on eight medical image datasets,we demonstrate that BSDA-Mamba outperforms existing models in accuracy,area under the curve,and F1-score.Our results highlight BSDA-Mamba’s potential as a reliable tool for medical image analysis,particularly in handling diverse imaging modalities from X-rays to MRI.The open-sourcing of our model’s code and datasets,will facilitate the reproduction and extension of our work.
基金funded by the project supported by the Heilongjiang Provincial Natural Science Foundation of China(Grant Number LH2022F030).
文摘3D model classification has emerged as a significant research focus in computer vision.However,traditional convolutional neural networks(CNNs)often struggle to capture global dependencies across both height and width dimensions simultaneously,leading to limited feature representation capabilities when handling complex visual tasks.To address this challenge,we propose a novel 3D model classification network named ViT-GE(Vision Transformer with Global and Efficient Attention),which integrates Global Grouped Coordinate Attention(GGCA)and Efficient Channel Attention(ECA)mechanisms.Specifically,the Vision Transformer(ViT)is employed to extract comprehensive global features from multi-view inputs using its self-attention mechanism,effectively capturing 3D shape characteristics.To further enhance spatial feature modeling,the GGCA module introduces a grouping strategy and global context interactions.Concurrently,the ECA module strengthens inter-channel information flow,enabling the network to adaptively emphasize key features and improve feature fusion.Finally,a voting mechanism is adopted to enhance classification accuracy,robustness,and stability.Experimental results on the ModelNet10 dataset demonstrate that our method achieves a classification accuracy of 93.50%,validating its effectiveness and superior performance.
文摘The theoretical framework of visual simulation in virtual reality is discussed. The new concept of visual image space is supposed. On the basis of visual image space, in visual perceptive sense, VR is considered as a spatial simulation. The objective of the spatial simulation is to transform physical space to visual image space. Last, the prototype system, surveying & mapping virtual Reality (SMVR), is developed, and the space simulation above is realized. By use of SMVR, the real 3D representation, 3D visual analysis, virtual plan and designs can be implemented.
文摘Purpose： We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes： First, to demonstrate the use of a science mapping approach to perform the review so that researchers may apply the procedure to the review of a scientific domain of their own interest, and second, to identify major areas of research activities concerning science mapping, intellectual milestones in the development of key specialties, evolutionary stages of major specialties involved, and the dynamics of transitions from one specialty to another.Design/methodology/approach： We first introduce a theoretical framework of the evolution of a scientific specialty. Then we demonstrate a generic search strategy that can be used to construct a representative dataset of bibliographic records of a domain of research. Next, progressively synthesized co-citation networks are constructed and visualized to aid visual analytic studies of the domain＇s structural and dynamic patterns and trends. Finally, trajectories of citations made by particular types of authors and articles are presented to illustrate the predictive potential of the analytic approach.Findings： The evolution of the science mapping research involves the development of a number of interrelated specialties. Four major specialties are discussed in detail in terms of four evolutionary stages： conceptualization, tool construction, application, and codification. Underlying connections between major specialties are also explored. The predictive analysis demonstrates citations trajectories of potentially transformative contributions.Research limitations： The systematic review is primarily guided by citation patterns in the dataset retrieved from the literature. The scope of the data is limited by the source of the retrieval, i.e. the Web of Science, and the composite query used. An iterative query refinement is possible if one would like to improve the data quality, although the current approach serves our purpose adequately. More in-depth analyses of each specialty would be more revealing by incorporating additional methods such as citation context analysis and studies of other aspects of scholarly publications.Practical implications： The underlying analytic process of science mapping serves many practical needs, notably bibliometric mapping, knowledge domain visualization, and visualization of scientific literature. In order to master such a complex process of science mapping, researchers often need to develop a diverse set of skills and knowledge that may span multiple disciplines. The approach demonstrated in this article provides a generic method for conducting a systematic review.Originality/value： Incorporating the evolutionary stages of a specialty into the visual analytic study of a research domain is innovative. It provides a systematic methodology for researchers to achieve a good understanding of how scientific fields evolve, to recognize potentially insightful patterns from visually encoded signs, and to synthesize various information so as to capture the state of the art of the domain.
基金Supported by the Major State Basic Research Development Program of China(2012CB720500)the National Natural Science Foundation of China(6133301021276078)+3 种基金the National Science Fund for Outstanding Young Scholars(61222303)the Fundamental Research Funds for the Central Universities,Shanghai Rising-Star Program(13QH1401200)the Program for New Century Excellent Talents in University(NCET-10-0885)Shanghai R&D Platform Construction Program(13DZ2295300)
文摘Purified terephthalic acid(PTA) is an important chemical raw material. P-xylene(PX) is transformed to terephthalic acid(TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a complex process involving three-phase reaction of gas, liquid and solid. To monitor the process and to improve the product quality, as well as to visualize the fault type clearly, a fault diagnosis method based on selforganizing map(SOM) and high dimensional feature extraction method, local tangent space alignment(LTSA),is proposed. In this method, LTSA can reduce the dimension and keep the topology information simultaneously,and SOM distinguishes various states on the output map. Monitoring results of PX oxidation reaction process indicate that the LTSA–SOM can well detect and visualize the fault type.
