目的散焦模糊检测致力于区分图像中的清晰与模糊像素,广泛应用于诸多领域,是计算机视觉中的重要研究方向。待检测图像含复杂场景时,现有的散焦模糊检测方法存在精度不够高、检测结果边界不完整等问题。本文提出一种由粗到精的多尺度散...目的散焦模糊检测致力于区分图像中的清晰与模糊像素,广泛应用于诸多领域,是计算机视觉中的重要研究方向。待检测图像含复杂场景时,现有的散焦模糊检测方法存在精度不够高、检测结果边界不完整等问题。本文提出一种由粗到精的多尺度散焦模糊检测网络,通过融合不同尺度下图像的多层卷积特征提高散焦模糊的检测精度。方法将图像缩放至不同尺度,使用卷积神经网络从每个尺度下的图像中提取多层卷积特征,并使用卷积层融合不同尺度图像对应层的特征;使用卷积长短时记忆(convolutional long-short term memory,Conv-LSTM)层自顶向下地整合不同尺度的模糊特征,同时生成对应尺度的模糊检测图,以这种方式将深层的语义信息逐步传递至浅层网络;在此过程中,将深浅层特征联合,利用浅层特征细化深一层的模糊检测结果;使用卷积层将多尺度检测结果融合得到最终结果。本文在网络训练过程中使用了多层监督策略确保每个Conv-LSTM层都能达到最优。结果在DUT(Dalian University of Technology)和CUHK(The Chinese University of Hong Kong)两个公共的模糊检测数据集上进行训练和测试,对比了包括当前最好的模糊检测算法BTBCRL(bottom-top-bottom network with cascaded defocus blur detection map residual learning),De Fusion Net(defocus blur detection network via recurrently fusing and refining multi-scale deep features)和DHDE(multi-scale deep and hand-crafted features for defocus estimation)等10种算法。实验结果表明:在DUT数据集上,本文模型相比于De Fusion Net模型,MAE(mean absolute error)值降低了38.8%,F0.3值提高了5.4%;在CUHK数据集上,相比于LBP(local binary pattern)算法,MAE值降低了36.7%,F0.3值提高了9.7%。通过实验对比,充分验证了本文提出的散焦模糊检测模型的有效性。结论本文提出的由粗到精的多尺度散焦模糊检测方法,通过融合不同尺度图像的特征,以及使用卷积长短时记忆层自顶向下地整合深层的语义信息和浅层的细节信息,使得模型在不同的图像场景中能得到更加准确的散焦模糊检测结果。展开更多
Heart disease is a leading cause ofmortality worldwide.Electrocardiograms(ECG)play a crucial role in diagnosing heart disease.However,interpreting ECGsignals necessitates specialized knowledge and training.The develop...Heart disease is a leading cause ofmortality worldwide.Electrocardiograms(ECG)play a crucial role in diagnosing heart disease.However,interpreting ECGsignals necessitates specialized knowledge and training.The development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease diagnosis.This research paper proposes a 3D Convolutional Long Short-Term Memory(Conv-LSTM)model for detecting heart disease using ECG signals.The proposed model combines the advantages of both convolutional neural networks(CNN)and long short-term memory(LSTM)networks.By considering both the spatial and temporal dependencies of ECG,the 3D Conv-LSTM model enables the detection of subtle changes in the signal over time.The model is trained on a dataset of ECG recordings from patients with various heart conditions,including arrhythmia,myocardial infarction,and heart failure.Experimental results show that the proposed 3D Conv-LSTM model outperforms traditional 2D CNN models in detecting heart disease,achieving an accuracy of 88%in the classification of five classes.Furthermore,themodel outperforms the other state-of-the-art deep learning models for ECG-based heart disease detection.Moreover,the proposedConv-LSTMnetwork yields highly accurate outcomes in identifying abnormalities in specific ECG leads.The proposed 3D Conv-LSTM model holds promise as a valuable tool for automated heart disease detection and diagnosis.This study underscores the significance of incorporating spatial and temporal dependencies in ECG-based heart disease detection.It highlights the potential of deep-learning models in enhancing the accuracy and efficiency of diagnosis.展开更多
Accurate histopathology classification is a crucial factor in the diagnosis and treatment of Cholangiocarcinoma(CCA).Hyperspectral images(HSI)provide rich spectral information than ordinary RGB images,making them more...Accurate histopathology classification is a crucial factor in the diagnosis and treatment of Cholangiocarcinoma(CCA).Hyperspectral images(HSI)provide rich spectral information than ordinary RGB images,making them more useful for medical diagnosis.The Convolutional Neural Network(CNN)is commonly employed in hyperspectral image classification due to its remarkable capacity for feature extraction and image classification.However,many existing CNN-based HSI classification methods tend to ignore the importance of image spatial context information and the interdependence between spectral channels,leading to unsatisfied classification performance.Thus,to address these issues,this paper proposes a Spatial-Spectral Joint Network(SSJN)model for hyperspectral image classification that utilizes spatial self-attention and spectral feature extraction.The SSJN model is derived from the ResNet18 network and implemented with the non-local and Coordinate Attention(CA)modules,which extract long-range dependencies on image space and enhance spatial features through the Branch Attention(BA)module to emphasize the region of interest.Furthermore,the SSJN model employs Conv-LSTM modules to extract long-range depen-dencies in the image spectral domain.This addresses the gradient disappearance/explosion phenom-ena and enhances the model classification accuracy.The experimental results show that the pro-posed SSJN model is more efficient in leveraging the spatial and spectral information of hyperspec-tral images on multidimensional microspectral datasets of CCA,leading to higher classification accuracy,and may have useful references for medical diagnosis of CCA.展开更多
文摘目的散焦模糊检测致力于区分图像中的清晰与模糊像素,广泛应用于诸多领域,是计算机视觉中的重要研究方向。待检测图像含复杂场景时,现有的散焦模糊检测方法存在精度不够高、检测结果边界不完整等问题。本文提出一种由粗到精的多尺度散焦模糊检测网络,通过融合不同尺度下图像的多层卷积特征提高散焦模糊的检测精度。方法将图像缩放至不同尺度,使用卷积神经网络从每个尺度下的图像中提取多层卷积特征,并使用卷积层融合不同尺度图像对应层的特征;使用卷积长短时记忆(convolutional long-short term memory,Conv-LSTM)层自顶向下地整合不同尺度的模糊特征,同时生成对应尺度的模糊检测图,以这种方式将深层的语义信息逐步传递至浅层网络;在此过程中,将深浅层特征联合,利用浅层特征细化深一层的模糊检测结果;使用卷积层将多尺度检测结果融合得到最终结果。本文在网络训练过程中使用了多层监督策略确保每个Conv-LSTM层都能达到最优。结果在DUT(Dalian University of Technology)和CUHK(The Chinese University of Hong Kong)两个公共的模糊检测数据集上进行训练和测试,对比了包括当前最好的模糊检测算法BTBCRL(bottom-top-bottom network with cascaded defocus blur detection map residual learning),De Fusion Net(defocus blur detection network via recurrently fusing and refining multi-scale deep features)和DHDE(multi-scale deep and hand-crafted features for defocus estimation)等10种算法。实验结果表明:在DUT数据集上,本文模型相比于De Fusion Net模型,MAE(mean absolute error)值降低了38.8%,F0.3值提高了5.4%;在CUHK数据集上,相比于LBP(local binary pattern)算法,MAE值降低了36.7%,F0.3值提高了9.7%。通过实验对比,充分验证了本文提出的散焦模糊检测模型的有效性。结论本文提出的由粗到精的多尺度散焦模糊检测方法,通过融合不同尺度图像的特征,以及使用卷积长短时记忆层自顶向下地整合深层的语义信息和浅层的细节信息,使得模型在不同的图像场景中能得到更加准确的散焦模糊检测结果。
基金supported by the research project—Application of Machine Learning Methods for Early Diagnosis of Pathologies of the Cardiovascular System funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan.Grant No.IRN AP13068289.The supervisor of the project is Batyrkhan Omarov.
文摘Heart disease is a leading cause ofmortality worldwide.Electrocardiograms(ECG)play a crucial role in diagnosing heart disease.However,interpreting ECGsignals necessitates specialized knowledge and training.The development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease diagnosis.This research paper proposes a 3D Convolutional Long Short-Term Memory(Conv-LSTM)model for detecting heart disease using ECG signals.The proposed model combines the advantages of both convolutional neural networks(CNN)and long short-term memory(LSTM)networks.By considering both the spatial and temporal dependencies of ECG,the 3D Conv-LSTM model enables the detection of subtle changes in the signal over time.The model is trained on a dataset of ECG recordings from patients with various heart conditions,including arrhythmia,myocardial infarction,and heart failure.Experimental results show that the proposed 3D Conv-LSTM model outperforms traditional 2D CNN models in detecting heart disease,achieving an accuracy of 88%in the classification of five classes.Furthermore,themodel outperforms the other state-of-the-art deep learning models for ECG-based heart disease detection.Moreover,the proposedConv-LSTMnetwork yields highly accurate outcomes in identifying abnormalities in specific ECG leads.The proposed 3D Conv-LSTM model holds promise as a valuable tool for automated heart disease detection and diagnosis.This study underscores the significance of incorporating spatial and temporal dependencies in ECG-based heart disease detection.It highlights the potential of deep-learning models in enhancing the accuracy and efficiency of diagnosis.
基金supported by National Natural Science Foundation of China(No.62101040).
文摘Accurate histopathology classification is a crucial factor in the diagnosis and treatment of Cholangiocarcinoma(CCA).Hyperspectral images(HSI)provide rich spectral information than ordinary RGB images,making them more useful for medical diagnosis.The Convolutional Neural Network(CNN)is commonly employed in hyperspectral image classification due to its remarkable capacity for feature extraction and image classification.However,many existing CNN-based HSI classification methods tend to ignore the importance of image spatial context information and the interdependence between spectral channels,leading to unsatisfied classification performance.Thus,to address these issues,this paper proposes a Spatial-Spectral Joint Network(SSJN)model for hyperspectral image classification that utilizes spatial self-attention and spectral feature extraction.The SSJN model is derived from the ResNet18 network and implemented with the non-local and Coordinate Attention(CA)modules,which extract long-range dependencies on image space and enhance spatial features through the Branch Attention(BA)module to emphasize the region of interest.Furthermore,the SSJN model employs Conv-LSTM modules to extract long-range depen-dencies in the image spectral domain.This addresses the gradient disappearance/explosion phenom-ena and enhances the model classification accuracy.The experimental results show that the pro-posed SSJN model is more efficient in leveraging the spatial and spectral information of hyperspec-tral images on multidimensional microspectral datasets of CCA,leading to higher classification accuracy,and may have useful references for medical diagnosis of CCA.
