In recent years,with the rapid development of e-commerce,people need to classify the wide variety and a large number of clothing images appearing on e-commerce platforms.In order to solve the problems of long time con...In recent years,with the rapid development of e-commerce,people need to classify the wide variety and a large number of clothing images appearing on e-commerce platforms.In order to solve the problems of long time consumption and unsatisfactory classification accuracy arising from the classification of a large number of clothing images,researchers have begun to exploit deep learning techniques instead of traditional learning methods.The paper explores the use of convolutional neural networks(CNNs)for feature learning to enhance global feature information interactions by adding an improved hybrid attention mechanism(HAM)that fully utilizes feature weights in three dimensions:channel,height,and width.Moreover,the improved pooling layer not only captures local feature information,but also fuses global and local information to improve the misclassification problem that occurs between similar categories.Experiments on the Fashion-MNIST and DeepFashion datasets show that the proposed method significantly improves the accuracy of clothing classification(93.62%and 67.9%)compared with residual network(ResNet)and convolutional block attention module(CBAM).展开更多
With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming a...With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.展开更多
Semantic segmentation is for pixel-level classification tasks,and contextual information has an important impact on the performance of segmentation.In order to capture richer contextual information,we adopt ResNet as ...Semantic segmentation is for pixel-level classification tasks,and contextual information has an important impact on the performance of segmentation.In order to capture richer contextual information,we adopt ResNet as the backbone network and designs an encoder-decoder architecture based on multidimensional attention(MDA)module and multiscale upsampling(MSU)module.The MDA module calculates the attention matrices of the three dimensions to capture the dependency of each position,and adaptively captures the image features.The MSU module adopts parallel branches to capture the multiscale features of the images,and multiscale feature aggregation can enhance contextual information.A series of experiments demonstrate the validity of the model on Cityscapes and Camvid datasets.展开更多
Prediction of enhancer-promoter interactions(EPIs)is key to regulating gene expression and diagnosing genetic diseases.Due to limited resolution,biological experiments perform not as well as expected while precisely i...Prediction of enhancer-promoter interactions(EPIs)is key to regulating gene expression and diagnosing genetic diseases.Due to limited resolution,biological experiments perform not as well as expected while precisely identifying specific interactions,giving rise to computational biology approaches.Many EPI predictors have been developed,but their prediction accuracy still needs to be enhanced.Here,we design a new model named EPIMR to identify enhancer-promoter interactions.First,Hilbert Curve is utilized to represent sequences to images to preserve the position and spatial information.Second,a multi-scale residual neural network(ResNet)is used to learn the distinguishing features of different abstraction levels.Finally,matching heuristics are adopted to concatenate the learned features of enhancers and promoters,which pays attention to their potential interaction information.Experimental results on six cell lines indicate that EPIMR performs better than existing methods,with higher area under the precision-recall curve(AUPR)and area under the receiver operating characteristic(AUROC)results on benchmark and under-sampling datasets.Furthermore,our model is pre-trained on all cell lines,which improves not only the transferability of cross-cell line prediction,but also cell line-specific prediction ability.In conclusion,our method serves as a valuable technical tool for predicting enhancer-promoter interactions,contributing to the understanding of gene transcription mechanisms.Our code and results are available at https://github.com/guofei-tju/EPIMR.展开更多
Glaucoma is a prevalent cause of blindness worldwide.If not treated promptly,it can cause vision and quality of life to deteriorate.According to statistics,glaucoma affects approximately 65 million individuals globall...Glaucoma is a prevalent cause of blindness worldwide.If not treated promptly,it can cause vision and quality of life to deteriorate.According to statistics,glaucoma affects approximately 65 million individuals globally.Fundus image segmentation depends on the optic disc(OD)and optic cup(OC).This paper proposes a computational model to segment and classify retinal fundus images for glaucoma detection.Different data augmentation techniques were applied to prevent overfitting while employing several data pre-processing approaches to improve the image quality and achieve high accuracy.The segmentation models are based on an attention U-Net with three separate convolutional neural networks(CNNs)backbones:Inception-v3,visual geometry group 19(VGG19),and residual neural network 50(ResNet50).The classification models also employ a modified version of the above three CNN architectures.Using the RIM-ONE dataset,the attention U-Net with the ResNet50 model as the encoder backbone,achieved the best accuracy of 99.58%in segmenting OD.The Inception-v3 model had the highest accuracy of 98.79%for glaucoma classification among the evaluated segmentation,followed by the modified classification architectures.展开更多
文摘In recent years,with the rapid development of e-commerce,people need to classify the wide variety and a large number of clothing images appearing on e-commerce platforms.In order to solve the problems of long time consumption and unsatisfactory classification accuracy arising from the classification of a large number of clothing images,researchers have begun to exploit deep learning techniques instead of traditional learning methods.The paper explores the use of convolutional neural networks(CNNs)for feature learning to enhance global feature information interactions by adding an improved hybrid attention mechanism(HAM)that fully utilizes feature weights in three dimensions:channel,height,and width.Moreover,the improved pooling layer not only captures local feature information,but also fuses global and local information to improve the misclassification problem that occurs between similar categories.Experiments on the Fashion-MNIST and DeepFashion datasets show that the proposed method significantly improves the accuracy of clothing classification(93.62%and 67.9%)compared with residual network(ResNet)and convolutional block attention module(CBAM).
文摘With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.
基金Fundamental Research Fund in Heilongjiang Provincial Universities(Nos.135409602,135409102)。
文摘Semantic segmentation is for pixel-level classification tasks,and contextual information has an important impact on the performance of segmentation.In order to capture richer contextual information,we adopt ResNet as the backbone network and designs an encoder-decoder architecture based on multidimensional attention(MDA)module and multiscale upsampling(MSU)module.The MDA module calculates the attention matrices of the three dimensions to capture the dependency of each position,and adaptively captures the image features.The MSU module adopts parallel branches to capture the multiscale features of the images,and multiscale feature aggregation can enhance contextual information.A series of experiments demonstrate the validity of the model on Cityscapes and Camvid datasets.
基金supported by the National Key R&D Program of China(No.2021YFC2100700)National Natural Science Foundation of China(NSFC)(Nos.62322215 and 62172296)+3 种基金Excellent Young Scientists Fund in Hunan Province(No.2022JJ20077)Scientific Research Fund of Hunan Provincial Education Department(No.22A0007)Shenzhen Science and Technology Program(No.KQTD20200820113106007)High Performance Computing Center of Central South University.
文摘Prediction of enhancer-promoter interactions(EPIs)is key to regulating gene expression and diagnosing genetic diseases.Due to limited resolution,biological experiments perform not as well as expected while precisely identifying specific interactions,giving rise to computational biology approaches.Many EPI predictors have been developed,but their prediction accuracy still needs to be enhanced.Here,we design a new model named EPIMR to identify enhancer-promoter interactions.First,Hilbert Curve is utilized to represent sequences to images to preserve the position and spatial information.Second,a multi-scale residual neural network(ResNet)is used to learn the distinguishing features of different abstraction levels.Finally,matching heuristics are adopted to concatenate the learned features of enhancers and promoters,which pays attention to their potential interaction information.Experimental results on six cell lines indicate that EPIMR performs better than existing methods,with higher area under the precision-recall curve(AUPR)and area under the receiver operating characteristic(AUROC)results on benchmark and under-sampling datasets.Furthermore,our model is pre-trained on all cell lines,which improves not only the transferability of cross-cell line prediction,but also cell line-specific prediction ability.In conclusion,our method serves as a valuable technical tool for predicting enhancer-promoter interactions,contributing to the understanding of gene transcription mechanisms.Our code and results are available at https://github.com/guofei-tju/EPIMR.
文摘Glaucoma is a prevalent cause of blindness worldwide.If not treated promptly,it can cause vision and quality of life to deteriorate.According to statistics,glaucoma affects approximately 65 million individuals globally.Fundus image segmentation depends on the optic disc(OD)and optic cup(OC).This paper proposes a computational model to segment and classify retinal fundus images for glaucoma detection.Different data augmentation techniques were applied to prevent overfitting while employing several data pre-processing approaches to improve the image quality and achieve high accuracy.The segmentation models are based on an attention U-Net with three separate convolutional neural networks(CNNs)backbones:Inception-v3,visual geometry group 19(VGG19),and residual neural network 50(ResNet50).The classification models also employ a modified version of the above three CNN architectures.Using the RIM-ONE dataset,the attention U-Net with the ResNet50 model as the encoder backbone,achieved the best accuracy of 99.58%in segmenting OD.The Inception-v3 model had the highest accuracy of 98.79%for glaucoma classification among the evaluated segmentation,followed by the modified classification architectures.
