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Proceedings of the 2020 9th International Conference on Bioinformatics and Biomedical Science

作者： Ying Bai Haifeng Zhao Shaojie Zhang Dong Nie Zhenyu Tang School of Computer Science and Technology Anhui University Department of Computer Science University of North Carolina at Chapel Hill Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

ISBN: (纸本)9781450388658

Image noise is an inherent issue in low-dose CT (LDCT). Increasing radiation dose can alleviate this problem to some extent, but it also brings potential risks to the patients. Thus, LDCT denoising has raised increasing attention from researchers. Currently, many deep learning based LDCT denoising methods have been proposed with success, such as encoder-decoder. In this paper, we propose a novel multi-scale hierarchy feature fusion based encoder-decoder network within the GAN framework for LDCT denoising. Specifically, a four-stage multi-scale dilated blocks is introduced to integrate low-level features with high-level features. Comparing with the conventional skip connection, which ignores the semantic gap between low-level features and high-level features, the advantage of our method is the effective use of low-level information. In addition, residual learning is also adopted to boost the training of the network. Experimental results on public dataset have demonstrated the superiority of our method over the state-of-the-art methods under comparison in both visual quality and quantitative evaluation.

关键词： Image denoising Low-dose CT encoder-decoder Deep learning Wasserstein GAN

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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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Neural Image Caption Generation with Weighted Training and Reference

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COGNITIVE COMPUTATION 2019年第6期11卷 763-777页

作者： Ding, Guiguang Chen, Minghai Zhao, Sicheng Chen, Hui Han, Jungong Liu, Qiang Tsinghua Univ Sch Software Beijing 100084 Peoples R China Univ Lancaster Comp & Commun Lancaster England

Image captioning, which aims to automatically generate a sentence description for an image, has attracted much research attention in cognitive computing. The task is rather challenging, since it requires cognitively combining the techniques from both computer vision and natural language processing domains. Existing CNN-RNN framework-based methods suffer from two main problems: in the training phase, all the words of captions are treated equally without considering the importance of different words;in the caption generation phase, the semantic objects or scenes might be misrecognized. In our paper, we propose a method based on the encoder-decoder framework, named Reference based Long Short Term Memory (R-LSTM), aiming to lead the model to generate a more descriptive sentence for the given image by introducing reference information. Specifically, we assign different weights to the words according to the correlation between words and images during the training phase. We additionally maximize the consensus score between the captions generated by the captioning model and the reference information from the neighboring images of the target image, which can reduce the misrecognition problem. We have conducted extensive experiments and comparisons on the benchmark datasets MS COCO and Flickr30k. The results show that the proposed approach can outperform the state-of-the-art approaches on all metrics, especially achieving a 10.37% improvement in terms of CIDEr on MS COCO. By analyzing the quality of the generated captions, we come to a conclusion that through the introduction of reference information, our model can learn the key information of images and generate more trivial and relevant words for images.

关键词： Image captioning Reference Long short-term memory encoder-decoder

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Refined Spatial Network for Human Action Recognition

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IEEE ACCESS 2019年 7卷 111043-111052页

作者： Wu, Chunlei Cao, Haiwen Zhang, Weishan Wang, Leiquan Wei, Yiwei Peng, Zexin China Univ Petr Coll Comp & Commun Engn Qingdao 266580 Shandong Peoples R China China Univ Petr Sch Petr Engn Beijing 102249 Peoples R China

Effective video representation is a key ingredient in action recognition, but how to learn effective spatial features remains a fundamental and challenging task. The existing CNN-based methods apply low-resolution feature maps to get the high-level semantic labels. However, the slenderer spatial information for action representation has lost. In this paper, we propose a novel stacked spatial network (SSN), which integrates multi-layer feature maps in an end-to-end manner. Spatial features extraction network based on encoder-decoder is firstly used to obtain multi-level and multi-resolution spatial features under the supervision of high-level sematic labels. The multi-level features are aggregated through a stacked spatial fusion layer, which intrinsically refines the traditional convolutional neural network. Then, refined spatial network (RSN) is proposed to aggregate spatial network and SSN. Particularly, the learned representation of RSN comprises two components for representing semantic label information and local slenderer spatial information. Extensive experimental results on UCF-101 and HMDB-51 datasets demonstrate the effectiveness of the proposed RSN.

关键词： Action recognition encoder-decoder spatial features

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Attention-Based Convolutional LSTM for Describing Video

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IEEE ACCESS 2020年 8卷 133713-133724页

作者： Liu, Zhongyu Chen, Tian Ding, Enjie Liu, Yafeng Yu, Wanli China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221008 Jiangsu Peoples R China China Univ Min & Technol IOT Percept Mine Res Ctr Xuzhou 221000 Jiangsu Peoples R China Univ Bremen Inst Electrodynam & Microelect D-28359 Bremen Germany

Video description technique has been widely used in the computer community for many applications. The typical approaches are mainly based on the encode-decode framework: the fixed-length video representation vectors are extracted by the encoder using the upper layer output of pre-trained convolutional neural networks (CNNs);The decoder uses the recurrent neural networks to generate a textual sentence. However, the upper layers of convolutional neural networks contain low-resolution, semantically strong, while the lower layers contain high-resolution, semantically weak features. In the existing method, the multi-scale information of CNNs is hardly considered to be used in the video description. Ignoring this information will lead to the problem that the video description is not detailed and comprehensive. This paper applies the hierarchical convolutional long short-term memory (ConvLSTM) in the encode-decode framework to conduct feature extraction of the upper and lower layers in CNNs. Moreover, multiple network structures are designed to explore the Spatio-temporal feature extraction performance of ConvLSTM, which can approach the optimal accuracy in the three-layer ConvLSTM. In order to efficiently improve the language quality of video description, the attention mechanism focuses on visual feature output by ConvLSTM. The extensive experimental results demonstrate that the proposed method outperforms the existing approaches.

关键词： Feature extraction Visualization Decoding Task analysis Semantics Recurrent neural networks Convolution Video description long short-term memory encoder-decoder attention mechanism

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Hybrid Attention Distribution and Factorized Embedding Matrix in Image Captioning

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IEEE ACCESS 2020年 8卷 154453-154460页

作者： Wang, Jian Feng, Jie Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou 310018 Peoples R China

Self-attention mechanisms are widely used in current encoder-decoder frameworks of image-captioning tasks. These mechanisms use the transformer module as the basic unit to compute the similarity between query and key vectors, and normalize them to obtain the attention distribution matrix, weighting and reconstructing the rich semantic query vectors. However, a single attention distribution matrix cannot express more complex intrinsic relations between query and key vectors. Some very important intrinsic relations can thus become lost. We therefore propose a hybrid attention distribution (HAD) that allows multiple distributions to be reconstructed to express deeper internal relations and avoids a single shallow attention distribution. In addition, to reduce the number of model parameters, we factorize the word-embedding matrix. This effectively increases the training efficiency and prevents the metrics from decreasing. We extensively evaluate our proposals using the MS-COCO image-captioning dataset. Our results outperform existing state-of-the-art methods on some metrics.

关键词： Self-attention image captioning transformer encoder-decoder

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Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI

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SENSORS 2020年第11期20卷 3183页

作者： Khan, Zia Yahya, Norashikin Alsaih, Khaled Ali, Syed Saad Azhar Meriaudeau, Fabrice Univ Teknol PETRONAS Ctr Intelligent Signal & Imaging Res CISIR Dept Elect & Elect Engn Seri Iskandar 32610 Perak Malaysia Univ Burgundy ImViA ITFIM F-21078 Dijon France

In this paper, we present an evaluation of four encoder-decoder CNNs in the segmentation of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected CNNs are FCN, SegNet, U-Net, and DeepLabV3+, which was originally proposed for the segmentation of road scene, biomedical, and natural images. Segmentation of prostate in T2W MRI images is an important step in the automatic diagnosis of prostate cancer to enable better lesion detection and staging of prostate cancer. Therefore, many research efforts have been conducted to improve the segmentation of the prostate gland in MRI images. The main challenges of prostate gland segmentation are blurry prostate boundary and variability in prostate anatomical structure. In this work, we investigated the performance of encoder-decoder CNNs for segmentation of prostate gland in T2W MRI. Image pre-processing techniques including image resizing, center-cropping and intensity normalization are applied to address the issues of inter-patient and inter-scanner variability as well as the issue of dominating background pixels over prostate pixels. In addition, to enrich the network with more data, to increase data variation, and to improve its accuracy, patch extraction and data augmentation are applied prior to training the networks. Furthermore, class weight balancing is used to avoid having biased networks since the number of background pixels is much higher than the prostate pixels. The class imbalance problem is solved by utilizing weighted cross-entropy loss function during the training of the CNN model. The performance of the CNNs is evaluated in terms of the Dice similarity coefficient (DSC) and our experimental results show that patch-wise DeepLabV3+ gives the best performance with DSC equal to 92.8%. This value is the highest DSC score compared to the FCN, SegNet, and U-Net that also competed the recently published state-of-the-art method of prostate segmentation.

关键词： encoder-decoder CNNs DNN FCN SegNet U-Net DeepLabV3+

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A Multi-Level Attention Model for Remote Sensing Image Captions

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REMOTE SENSING 2020年第6期12卷

作者： Li, Yangyang Fang, Shuangkang Jiao, Licheng Liu, Ruijiao Shang, Ronghua Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist EducSch Artificial Intelligence Int Res Ctr Intelligent Percept & ComputatJoint Xian 710071 Peoples R China

The task of image captioning involves the generation of a sentence that can describe an image appropriately, which is the intersection of computer vision and natural language. Although the research on remote sensing image captions has just started, it has great significance. The attention mechanism is inspired by the way humans think, which is widely used in remote sensing image caption tasks. However, the attention mechanism currently used in this task is mainly aimed at images, which is too simple to express such a complex task well. Therefore, in this paper, we propose a multi-level attention model, which is a closer imitation of attention mechanisms of human beings. This model contains three attention structures, which represent the attention to different areas of the image, the attention to different words, and the attention to vision and semantics. Experiments show that our model has achieved better results than before, which is currently state-of-the-art. In addition, the existing datasets for remote sensing image captioning contain a large number of errors. Therefore, in this paper, a lot of work has been done to modify the existing datasets in order to promote the research of remote sensing image captioning.

关键词： remote sensing image image caption attention encoder-decoder

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Trace2trace-A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories

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SENSORS 2020年第12期20卷 3503页

作者： Crivellari, Alessandro Beinat, Euro Univ Salzburg Dept Geoinformat Z GIS A-5020 Salzburg Austria

Neural machine translation is a prominent field in the computational linguistics domain. By leveraging the recent developments of deep learning, it gave birth to powerful algorithms for translating text from one language to another. This study aims to assess the feasibility of transferring the neural machine translation approach into a completely different context, namely human mobility and trajectory analysis. Building a conceptual parallelism between sentences (sequences of words) and motion traces (sequences of locations), we aspire to translate individual trajectories generated by a certain category of users into the corresponding mobility traces potentially generated by a different category of users. The experiment is inserted in the background of tourist mobility analysis, with the goal of translating the motion behavior of tourists belonging to a specific nationality into the motion behavior of tourists belonging to a different nationality. The model adopted is based on the seq2seq approach and consists of an encoder-decoder architecture based on long short-term memory (LSTM) neural networks and neural embeddings. The encoder turns an input location sequence into a corresponding hidden vector;the decoder reverses the process, turning the vector into an output location sequence. The proposed framework, tested on a real-world large-scale dataset, explores an effective attempt of motion transformation between different entities, arising as a potentially powerful source of mobility information disclosure, especially in the context of crowd management and smart city services.

关键词： seq2seq encoder-decoder neural networks LSTM trajectories motion behavior smart tourism

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Deep Image Captioning: An Overview 42

Deep Image Captioning: An Overview

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42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Hrga, I. Ivasic-Kos, M. Juraj Dobrila Univ Pula Fac Informat Pula Croatia Univ Rijeka Dept Informat Rijeka Croatia

ISBN: (纸本)9789532330984

Image captioning is a process of automatically describing an image with one or more natural language sentences. In recent years, image captioning has witnessed rapid progress, from initial template-based models to the current ones, based on deep neural networks. This paper gives an overview of issues and recent image captioning research, with a particular emphasis on models that use the deep encoder-decoder architecture. We discuss the advantages and disadvantages of different approaches, along with reviewing some of the most commonly used evaluation metrics and datasets.

关键词： image captioning encoder-decoder attention mechanism deep neural networks

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