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arXiv 2019年

作者： Xie, Yutong Zhang, Jianpeng Xia, Yong Shen, Chunhua National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi'an710072 China School of Computer Science University of Adelaide SA Australia

Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhancedSN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Fabrication of 1D nanometer tungsten trioxide under different solvent system

Fabrication of 1D nanometer tungsten trioxide under differen...

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Chinese Materials Conference, 2018

作者： Zhao, Linyan Shen, Yuegang Fan, Youshu Ma, Liwen Xi, Xiaoli National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China College of Material Science and Engineering Key Laboratory of Advanced Functional Materials Education Ministry of China Beijing University of Technology Beijing100124 China

ISBN: (纸本)9783035713848

As a cheap and stable transition metal oxide, the tungsten trioxide (WO3) has received extensive attentions due to superior physical and chemical properties and could be used in electronic devices, lithium-ion batteries, gas sensors, dye sensitized solar cells, catalysts. In this study, the well-designed 1D architecture of nanowires and nanorods was successfully synthesized via a simple and facile solvethermal method with no template or additives. It was found that both solvent type and concentration of W raw material could affect the size and morphology of WO3 significantly in a regular way. Different products showed distinct photocatalytic activities during the processing of degradation methylene blue under visible light. And finally the underlying reasons for the different photocatalytic activities were discussed. © 2019 Trans Tech Publications, Switzerland.

关键词： Tungsten compounds

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Are Noisy Sentences Useless for Distant Supervised Relation Extraction?

arXiv

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arXiv 2019年

作者： Shang, Yu-Ming Huang, He-Yan Mao, Xian-Ling Sun, Xin Wei, Wei School of Computer Science Beijing Institute of Technology Beijing China CETC Big Data Research Institute Co. Ltd. Guiyang550022 China Huazhong University of Science and Technology Hu’bei China Big Data Application on lmproving Government Governance Capabilities National Engineering Laboratory Guiyang550022 China

The noisy labeling problem has been one of the major obstacles for distant supervised relation extraction. Existing approaches usually consider that the noisy sentences are useless and will harm the model’s performance. Therefore, they mainly alleviate this problem by reducing the influence of noisy sentences, such as applying bag-level selective attention or removing noisy sentences from sentence-bags. However, the underlying cause of the noisy labeling problem is not the lack of useful information, but the missing relation labels. Intuitively, if we can allocate credible labels for noisy sentences, they will be transformed into useful training data and benefit the model’s performance. Thus, in this paper, we propose a novel method for distant supervised relation extraction, which employs unsupervised deep clustering to generate reliable labels for noisy sentences. Specifically, our model contains three modules: a sentence encoder, a noise detector and a label generator. The sentence encoder is used to obtain feature representations. The noise detector detects noisy sentences from sentence-bags, and the label generator produces high-confidence relation labels for noisy sentences. Extensive experimental results demonstrate that our model outperforms the state-of-the-art baselines on a popular benchmark dataset, and can indeed alleviate the noisy labeling problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal encoding

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Analyses of geographical observations in the Heihe River Basin: Perspectives from complexity theory

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Journal of Geographical Sciences 2019年第9期29卷 1441-1461页

作者： Gao Jianbo FANG Peng YUAN Lihua State Key Laboratory of Earth Surface Processes and Resource Ecology Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China Institute of Complexity Science and Big Data Technology Guangxi UniversityNanning 530004China Wuhan National Laboratory for Optoelectronics Key Laboratory of In formation Storage SystemEngineering Research Center of Data Storage Systems and TechnologyMinistry of Education of ChinaSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Center for GeoData and Analysis Faculty of Geographical ScieneeBeijing Normal UniversityBeijing 100875China

Since 2005,dozens of geographical observational stations have been established in the Heihe River Basin(HRB),and by now a large amount of meteorological,hydrological,and ecological observations as well as data pertaining to water resources,soil and vegetation have been *** adequately analyze these available data and data to be further collected in future,we present a perspective from complexity *** concrete materials covered include a presentation of adaptive multiscale filter,which can readily determine arbitrary trends,maximally reduce noise,and reliably perform fractal and multifractal analysis,and a presentation of scale-dependent Lyapunov exponent(SDLE),which can reliably distinguish deterministic chaos from random processes,determine the error doubling time for prediction,and obtain the defining parameters of the process *** adaptive filter is illustrated by applying it to obtain the global warming trend and the Atlantic multidecadal oscillation from sea surface temperature data,and by applying it to some variables collected at the HRB to determine diurnal cycle and fractal *** SDLE is illustrated to determine intermittent chaos from river flow data.

关键词： Heihe River basin geographical observation complexity theory adaptive multiscale filter fractal analysis scale-dependent Lyapunov exponent

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Person re-identification in aerial imagery

arXiv

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arXiv 2019年

作者： Zhang, Shizhou Zhang, Qi Yang, Yifei Wei, Xing Wang, Peng Jiao, Bingliang Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China

Nowadays, with the rapid development of consumer Unmanned Aerial Vehicles (UAVs), visual surveillance by utilizing the UAV platform has been very attractive. Most of the research works for UAV captured visual data are mainly focused on the tasks of object detection and tracking. However, limited attention has been paid to the task of person Re-identification (ReID) which has been widely studied in ordinary surveillance cameras with fixed emplacements. In this paper, to facilitate the research of person ReID in aerial imagery, we collect a large scale airborne person ReID dataset named as Person ReID for Aerial Imagery (PRAI-1581), which consists of 39,461 images of 1581 person identities. The images of the dataset are shot by two DJI consumer UAVs flying at an altitude ranging from 20 to 60 meters above the ground, which covers most of the real UAV surveillance scenarios. In addition, we propose to utilize subspace pooling of convolution feature maps to represent the input person images. Our method can learn a discriminative and compact feature representation for ReID in aerial imagery and can be trained in an end-to-end fashion efficiently. We conduct extensive experiments on the proposed dataset and the experimental results demonstrate that re-identify persons in aerial imagery is a challenging problem, where our method performs favorably against state of the arts. Our dataset can be accessed via https://***/stormyoung/PRAI-1581. Copyright © 2019, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Deep generative autoencoder for low-dimensional embeding extraction from single-cell RNAseq data

Deep generative autoencoder for low-dimensional embeding ext...

引用

IEEE International Conference on Bioinformatics and Biomedicine

作者： Shiquan Sun Yang Liu Xuequn Shang National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical UniversityXi'an ShaanxiPeople's Republic of China710129 School of Computer Science Northwestern Polytechnical Universit

ISBN: (纸本)9781728118680

Single-cell RNA sequencing (scRNAseq) can reveal biological diversity at the cellular level that are unexplored by bulk RNA sequencing (RNAseq), but they suffer from the excessive zero expression counts and the limitation of the scalability in practice. Here, we propose a non-linear generative autoencoder based method, scSVA, relying on an integration of variational autoencoder and dropout imputations. Specifically, scSVA automatically identifies the dropouts and recovery these values only to avoid introducing new biases. Then, scSVA utilizes stochastic optimization and deep neural network to extract the low-dimensional embedding from gene expression levels. We illustrate the benefits of scSVA through in-depth real analyses of six published scRNAseq data sets. scSVA is up to 1.3 times more powerful cell clustering accuracy than existing approaches. The high power of scSVA allows us to identify new cell types that reveal new biology from scRNAseq data that otherwise cannot be revealed by existing approaches.

关键词： bioinformatics cellular biophysics data analysis genetics genomics molecular biophysics neural nets pattern clustering RNA stochastic processes cellular biophysics molecular biology Neural network Markov chain Cluster analysis Bioinformatics Genomics data analysis Genetics RNA Stochastic Processes SINGLE CELL Analyses, Sequence RNA Sequence Statistical analysis

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H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task

arXiv

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arXiv 2020年

作者： Jia, Haozhe Cai, Weidong Huang, Heng Xia, Yong Research & Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China School of Computer Science University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States JD Finance America Corporation CaliforniaCA94043 United States

In this paper, we propose a Hybrid High-resolution and Non-local Feature Network (H2NF-Net) to segment brain tumor in multimodal MR images. Our H2NF-Net uses the single and cascaded HNF-Nets to segment different brain tumor sub-regions and combines the predictions together as the final segmentation. We trained and evaluated our model on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset. The results on the test set show that the combination of the single and cascaded models achieved average Dice scores of 0.78751, 0.91290, and 0.85461, as well as Hausdorff distances (95%) of 26.57525, 4.18426, and 4.97162 for the enhancing tumor, whole tumor, and tumor core, respectively. Our method won the second place in the BraTS 2020 challenge segmentation task out of nearly 80 participants. © 2020, CC BY.

关键词： Tumors

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Evaluating Local Geometric Feature Representations for 3D Rigid data Matching

arXiv

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arXiv 2019年

作者： Yang, Jiaqi Quan, Siwen Wang, Peng Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Poly-technical University Xi’an710129 China School of Electronic and Control Engineering Chang’An University Xi’an710064 China

—Local geometric descriptors remain an essential component for 3D rigid data matching and fusion. The devise of a rotational invariant local geometric descriptor usually consists of two steps: local reference frame (LRF) construction and feature representation. Existing evaluation efforts have mainly been paid on the LRF or the overall descriptor, yet the quantitative comparison of feature representations remains unexplored. This paper fills this gap by comprehensively evaluating nine state-of-the-art local geometric feature representations. Our evaluation is on the ground that ground-truth LRFs are leveraged such that the ranking of tested feature representations are more convincing as opposed to existing studies. The experiments are deployed on six standard datasets with various application scenarios (shape retrieval, point cloud registration, and object recognition) and data modalities (LiDAR, Kinect, and Space Time) as well as perturbations including Gaussian noise, shot noise, data decimation, clutter, occlusion, and limited overlap. The evaluated terms cover the major concerns for a feature representation, e.g., distinctiveness, robustness, compactness, and efficiency. The outcomes present interesting findings that may shed new light on this community and provide complementary perspectives to existing evaluations on the topic of local geometric feature description. A summary of evaluated methods regarding their peculiarities is also presented to guide real-world applications and new descriptor crafting. Copyright © 2019, The Authors. All rights reserved.

关键词： Shot noise

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A performance evaluation of correspondence grouping methods for 3D rigid data matching

arXiv

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arXiv 2019年

作者： Yang, Jiaqi Xian, Ke Wang, Peng Zhang, Yanning School of Computer Science Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710129 China National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision. While a number of correspondence selection methods have been proposed in recent years, their advantages and shortcomings remain unclear regarding different applications and perturbations. To fill this gap, this paper gives a comprehensive evaluation of nine state-of-the-art 3D correspondence grouping methods. A good correspondence grouping algorithm is expected to retrieve as many as inliers from initial feature matches, giving a rise in both precision and recall as well as facilitating accurate transformation estimation. Toward this rule, we deploy experiments on three benchmarks with different application contexts including shape retrieval, 3D object recognition, and point cloud registration together with various perturbations such as noise, point density variation, clutter, occlusion, partial overlap, different scales of initial correspondences, and different combinations of keypoint detectors and descriptors. The rich variety of application scenarios and nuisances result in different spatial distributions and inlier ratios of initial feature correspondences, thus enabling a thorough evaluation. Based on the outcomes, we give a summary of the traits, merits, and demerits of evaluated approaches and indicate some potential future research directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

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Associations of Serum 25-Hydroxyvitamin D Concentrations with Risks of Mortality and Cardiovascular Disease among Individuals with Psoriasis

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Journal of the American Academy of Dermatology 2025年

作者： Kaiqing Lin Ya Miao Lu Gan Bin Zhao Fang Fang Ru Wang Xiang Chen Jiaqi Huang National Clinical Research Center for Metabolic Diseases Metabolic Syndrome Research Center Key Laboratory of Diabetes Immunology Ministry of Education and Department of Metabolism and Endocrinology The Second Xiangya Hospital of Central South University Changsha Hunan 410011 China Xiangya School of Public Health Central South University Changsha China CSU-Sinocare Research Center for Nutrition and Metabolic Health Changsha China Furong Laboratory Changsha China. Electronic address: JIAQI.HUANG@***. Furong Laboratory Changsha China. Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden. School of Exercise and Health Shanghai University of Sport Shanghai China. Electronic address: WANGRU@***. Department of Dermatology Hunan Engineering Research Center of Skin Health and Disease Hunan Key Laboratory of Skin Cancer and Psoriasis Xiangya Clinical Research Center for Cancer Immunotherapy Xiangya Hospital Central South University Changsha Hunan China National Clinical Research Center for Geriatric Disorders Xiangya Hospital Central South University Changsha Hunan China National Engineering Laboratory of Medical Big Data Application Technology (Central South University) Changsha China Furong Laboratory Changsha China. Electronic address: CHENXIANGCK@***.

BACKGROUND:Evidence remains scarce regarding the relationship between serum 25-hydroxyvitamin D (25(OH)D) and the risk of mortality and cardiovascular disease (CVD) in psoriasis. OBJECTIVE:To explore the potential protective effect of serum 25(OH)D on the risk of mortality and CVD among patients with psoriasis. METHODS:This cohort study included 8,947 psoriasis patients and 178,937 controls identified at baseline in the UK Biobank cohort. Serum 25(OH)D levels were prospectively collected at the time of recruitment. Multivariable-adjusted Cox proportional hazards regression models were used to evaluate the associations between levels of serum 25(OH)D and risk of overall mortality and CVD incidence. RESULTS:Compared to the lowest level of serum 25(OH)D (<25 nmol/L), hazard ratios were 0.54 and 0.78 for overall mortality and incident CVD for the highest level of serum 25(OH)D (≥50 nmol/L) among psoriasis patients, respectively (all P<0.05). Psoriasis patients with serum 25(OH)D ≥50 nmol/L appeared to have no excess risk of mortality and CVD incidence, compared with matched controls. LIMITATIONS:Information on psoriasis subtypes was not available. CONCLUSION:A higher level of serum 25(OH)D was associated with a reduced risk of overall mortality and CVD in individuals with psoriasis.

关键词： cardiovascular disease cohort study epidemiology mortality psoriasis vitamin D

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