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Diagnostic Model of Coronary Microvascular Disease Combined With Full Convolution Deep Network With Balanced Cross-Entropy Cost Function

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IEEE ACCESS 2019年 7卷 177997-178006页

作者： Pan, Shiwen Zhang, Wei Zhang, Wanjun Xu, Liang Fan, Guohua Gong, Jianping Zhang, Bo Gu, Haibo Soochow Univ Affiliated Hosp 2 Suzhou 215000 Peoples R China

This paper addressed the vessel segmentation and disease diagnostic in coronary angiography image and proposed an encoder-decoder architecture of deep learning with End-to-End model, where encoder is based on ResNet, and the deep features are exacted automatically, and the decoder produces the segmentation result by balanced cross-entropy cost function. Furthermore, batch normalization is employed to decrease the gradient vanishing in the training process, so as to reduce the difficulty of training the deep neural network. The experiment results show that the algorithm effectively exacts the feature and edge information, therefore the complex background disturbance is suppressed convincingly, and the vessel segmentation precision is improved effectively, the segmentation precision for three typical vessels are 0.8365, 0.8924 and 0.6297 respectively;and the F-measure are 0.8514, 0.8786 and 0.7298, respectively. In addition, the experiment results show that our proposed can be generalized to the angiography image within limits.

关键词： Coronary microvascular cross-entropy cost function encoder-decoder deep learning batch normalization

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Repeated review based image captioning for image evidence review

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2018年 63卷 141-148页

作者： Guan, Jinning Wang, Eric Harbin Inst Technol Shenzhen Grad Sch Shenzhen Key Lab Internet Informat Collaborat Shenzhen 518055 Peoples R China

We propose a repeated review deep learning model for image captioning in image evidence review process. It consists of two subnetworks. One is the convolutional neural network which is employed to extract the image features and the other is the recurrent neural network which is used to decode the image features into captions. Our model combines the advantages of the two subnetworks by recalling visual information different from the traditional model of encoder-decoder, and then introduces multimodal layer to fuse the image and caption effectively. The proposed model has been validated on benchmark datasets (MSCOCO, Flick). It shows that the proposed model performs well on bleu-3 and bleu-4, even to some extent, beyond the best models available today (such as NIC, m-RNN, etc.).

关键词： Repeated review Image captioning encoder-decoder Multimodal

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High-Resolution Remote Sensing Imagery Classification of Imbalanced Data Using Multistage Sampling Method and Deep Neural Networks

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REMOTE SENSING 2019年第21期11卷

作者： Xia, Wei Ma, Caihong Liu, Jianbo Liu, Shibin Chen, Fu Yang, Zhi Duan, Jianbo Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 101408 Peoples R China Sanya Inst Remote Sensing Sanya 572029 Peoples R China China Elect Power Res Inst Co Ltd Beijing 100055 Peoples R China

Class imbalance is a key issue for the application of deep learning for remote sensing image classification because a model generated by imbalanced samples training has low classification accuracy for minority classes. In this study, an accurate classification approach using the multistage sampling method and deep neural networks was proposed to classify imbalanced data. We first balance samples by multistage sampling to obtain the training sets. Then, a state-of-the-art model is adopted by combining the advantages of atrous spatial pyramid pooling (ASPP) and encoder-decoder for pixel-wise classification, which are two different types of fully convolutional networks (FCNs) that can obtain contextual information of multiple levels in the encoder stage. The details and spatial dimensions of targets are restored using such information during the decoder stage. We employ four deep learning-based classification algorithms (basic FCN, FCN-8S, ASPP, and encoder-decoder with ASPP of our approach) on multistage training sets (original, MUS1, and MUS2) of WorldView-3 images in southeastern Qinghai-Tibet Plateau and GF-2 images in northeastern Beijing for comparison. The experiments show that, compared with existing sets (original, MUS1, and identical) and existing method (cost weighting), the MUS2 training set of multistage sampling significantly enhance the classification performance for minority classes. Our approach shows distinct advantages for imbalanced data.

关键词： high-resolution remote sensing image classification deep learning imbalanced data multistage sampling ASPP encoder-decoder

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Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and decoder for High-Resolution Remote Sensing Imagery

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REMOTE SENSING 2019年第1期11卷 20-20页

作者： Wang, Yuhao Liang, Binxiu Ding, Meng Li, Jiangyun Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China Thermo Fisher Sci Richardson TX 75081 USA

Dense semantic labeling is significant in high-resolution remote sensing imagery research and it has been widely used in land-use analysis and environment protection. With the recent success of fully convolutional networks (FCN), various types of network architectures have largely improved performance. Among them, atrous spatial pyramid pooling (ASPP) and encoder-decoder are two successful ones. The former structure is able to extract multi-scale contextual information and multiple effective field-of-view, while the latter structure can recover the spatial information to obtain sharper object boundaries. In this study, we propose a more efficient fully convolutional network by combining the advantages from both structures. Our model utilizes the deep residual network (ResNet) followed by ASPP as the encoder and combines two scales of high-level features with corresponding low-level features as the decoder at the upsampling stage. We further develop a multi-scale loss function to enhance the learning procedure. In the postprocessing, a novel superpixel-based dense conditional random field is employed to refine the predictions. We evaluate the proposed method on the Potsdam and Vaihingen datasets and the experimental results demonstrate that our method performs better than other machine learning or deep learning methods. Compared with the state-of-the-art DeepLab_v3+ our model gains 0.4% and 0.6% improvements in overall accuracy on these two datasets respectively.

关键词： remote sensing imagery dense semantic labeling fully convolutional networks atrous spatial pyramid pooling encoder-decoder superpixel-based DenseCRF

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Single Image Dehazing using CNN

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Procedia Computer Science 2019年 147卷 124-130页

作者： Huzaifa Rashid Nauman Zafar M Javed Iqbal Hassan Dawood Hussain Dawood Department of Software Engineering University of Engineering and Technology Taxila Pakistan Faculty of Computing and Information Technology University of Jeddah Jeddah Saudi Arabia

Haze is a natural phenomenon in which the dust, smoke and other particles alter the vision of the sky to reduce the visibility. Hazy images cause various visibility problems for traffic user, tourists everywhere, especially in hilly areas where haze and fog are very common. In this paper, a method for single image dehazing using convolutional neural network is proposed. Outdoor images have been used on which particular filters are applied to find the haze in image. Hazy images contain small value in only one-color alpha channel from Red, Blue, green RGB channel. The intensity of these pixels is mainly bestowed by air light depth map. Estimating these low value points of haze transmission map are useful to obtain a high quality dehazed image. An end-to-end encoder-decoder training model is utilized to achieve a high quality dehazed image. The approach is validated on datasets which consists of around 1500 outdoor images. The method also gives transmission map of the hazy image which can further be used to enhance visibility of the scene.

关键词： Image Dehazing Guided filter Transmission map Depth map Atmospheric light encoder-decoder

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Contextual label sensitive gated network for biomedical event trigger extraction

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JOURNAL OF BIOMEDICAL INFORMATICS 2019年第0期95卷 103221-000页

作者： Li, Lishuang Huang, Mengzuo Liu, Yang Qian, Shuang He, Xinyu Dalian Univ Technol Sch Comp Sci & Technol Dalian Peoples R China

Biomedical events play a key role in improving biomedical research. Event trigger identification, extracting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. There exist two main problems in previous methods: (1) The association among contextual trigger labels which can provide significant clues is ignored. (2)The weight between word embeddings and contextual features needs to be adjusted dynamically according to the trigger candidate. In this paper, we propose a novel contextual label sensitive gated network for biomedical event trigger extraction to solve the above two problems, which can mix the two parts dynamically and capture the contextual label clues automatically. Furthermore, we also introduce the dependency-based word embeddings to represent dependency-based semantic information as well as attention mechanism to get more focused representations. Experimental results show that our approach advances state-of-the-arts and achieves the best F1-score on the commonly used Multi-Level Event Extraction (MLEE) corpus.

关键词： Biomedical event trigger detection encoder-decoder Bi-GRU Gated mechanism

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ED-GAN:基于改进生成对抗网络的法律文本生成模型

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小型微型计算机系统 2019年第5期40卷 1020-1025页

作者：康云云彭敦陆陈章刘丛上海理工大学光电信息与计算机工程学院上海200093

法律文本的自动生成能缓解我国法律服务行业中的人力资源不足的问题,对抗生成网络模型的出现为法律文本的自动生成提供了新思路.本文提出一种基于对抗生成网络的文本自动生成模型——ED-GAN(Generative Adversarial Networks based on E... 详细信息

法律文本的自动生成能缓解我国法律服务行业中的人力资源不足的问题,对抗生成网络模型的出现为法律文本的自动生成提供了新思路.本文提出一种基于对抗生成网络的文本自动生成模型——ED-GAN(Generative Adversarial Networks based on encoder-decoder).在该模型的生成器中,首先将案情要素的关键词序列输入至编码器encoder阶段的LSTM中编码成一隐含层向量,再将这个隐含层向量输入到解码器decoder的LSTM中,并结合其各时间步的输出生成下一时间步的隐含层向量,进而得到各时间步的输出,生成文本序列.模型最后采用CNN网络来鉴别生成文本和真实文本之间的差距.实验验证表明,采用所提模型能够生成较理想的法律文本.

关键词：案情要素 GAN 文本自动生成 LSTMs encoder-decoder CNN

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RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

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GIGASCIENCE 2019年第11期8卷 giz123页

作者： Yasrab, Robail Atkinson, Jonathan A. Wells, Darren M. French, Andrew P. Pridmore, Tony P. Pound, Michael P. Univ Nottingham Sch Comp Sci Jubilee CampusWollaton Rd Nottingham NG8 1BB England Univ Nottingham Sch Biosci Sutton Bonington Campus Nottingham LE12 SRD England

Background: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. Results: We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. Conclusions: We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to

关键词： convolutional neural network (CNN) plant phenotyping computer vision encoder-decoder root system

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基于智能视觉的机械零件图像分割技术

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机械制造与自动化 2020年第5期49卷 203-206页

作者：洪庆宋乔杨晨涛张培常连立南京理工大学机械工程学院江苏南京210094 北京航天新风机械设备有限责任公司北京100854

为有效获取零件特征以提高现代生产智能化、精密化水平,基于智能视觉的机械零件分割研究起着关键作用。针对航天机器人等装配车间流水线零部件智能感知问题,研究基于智能视觉的零部件图像分割算法,实现机械零部件分割识别。基于Deeplabv... 详细信息

为有效获取零件特征以提高现代生产智能化、精密化水平,基于智能视觉的机械零件分割研究起着关键作用。针对航天机器人等装配车间流水线零部件智能感知问题,研究基于智能视觉的零部件图像分割算法,实现机械零部件分割识别。基于Deeplabv3图像分割算法提出一种增加自定义encoder-decoder特征提取模块的网络结构Deeplabv3-d,采用掩膜标记特征区域,基于该改进网络结构,采用mobileNet和Resnet101两种骨干网络进行零件图像分割对比实验,证明了该图像分割算法在零件图像分割应用领域的实用性。

关键词：智能制造深度学习图像分割 encoder-decoder Deeplabv3

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HybridNet: Classification and Reconstruction Cooperation for Semi-supervised Learning 1

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15th European Conference on Computer Vision (ECCV)

作者： Robert, Thomas Thome, Nicolas Cord, Matthieu Sorbonne Univ CNRS LIP6 F-75005 Paris France CEDRIC Conservatoire Natl Arts & Metiers F-75003 Paris France

ISBN: (数字)9783030012342

ISBN: (纸本)9783030012342;9783030012335

In this paper, we introduce a new model for leveraging unlabeled data to improve generalization performances of image classifiers: a two-branch encoder-decoder architecture called HybridNet. The first branch receives supervision signal and is dedicated to the extraction of invariant class-related representations. The second branch is fully unsupervised and dedicated to model information discarded by the first branch to reconstruct input data. To further support the expected behavior of our model, we propose an original training objective. It favors stability in the discriminative branch and complementarity between the learned representations in the two branches. HybridNet is able to outperform state-of-the-art results on CIFAR-10, SVHN and STL-10 in various semi-supervised settings. In addition, visualizations and ablation studies validate our contributions and the behavior of the model on both CIFAR-10 and STL-10 datasets.

关键词： Deep learning Semi-supervised learning Regularization Reconstruction Invariance and stability encoder-decoder

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