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The Construction and Measure Method of Dependency Parsing Tree Model

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The Construction and Measure Method of Dependency Parsing Tr...

IEEE International Conference on Software engineering and Service Sciences (ICSESS)

作者： Gang Liu Kai Wang Wangyang Liu Yang Cao College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China Big Data Application on lmproving Government Governance Capabilities National Engineering Laboratory CETC big data research institute company limited Guiyang Guizhou China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

Requirements analysis is the first point of information system development, which has a significant impact on the development. For the requirements of natural language description, automated requirement checking model cannot feasible. To verify the consistency of information system requirements, the paper builds a semantic model with tree nodes of natural language clauses. The model divides clauses into a representation of keywords set with seven-tuple. The paper not only proposed a dependency tree model to solve the problem that the refined tree cannot characterize the relationship between syntactic structure and keywords, but also put forward a dependency tagging algorithm and an algorithm to construct and update dependency parsing tree. The paper further put forward a semantic similarity calculation method to determine similarity among sub clause syntactic structures.

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Inter-slice context residual learning for 3D medical image segmentation

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arXiv 2020年

作者： Zhang, Jianpeng Xie, Yutong Wang, Yan Xia, Yong 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

Automated and accurate 3D medical image segmentation plays an essential role in assisting medical professionals to evaluate disease progresses and make fast therapeutic schedules. Although deep convolutional neural networks (DCNNs) have widely applied to this task, the accuracy of these models still need to be further improved mainly due to their limited ability to 3D context perception. In this paper, we propose the 3D context residual network (ConResNet) for the accurate segmentation of 3D medical images. This model consists of an encoder, a segmentation decoder, and a context residual decoder. We design the context residual module and use it to bridge both decoders at each scale. Each context residual module contains both context residual mapping and context attention mapping, the formal aims to explicitly learn the inter-slice context information and the latter uses such context as a kind of attention to boost the segmentation accuracy. We evaluated this model on the MICCAI 2018 Brain Tumor Segmentation (BraTS) dataset and NIH Pancreas Segmentation (Pancreas-CT) dataset. Our results not only demonstrate the effectiveness of the proposed 3D context residual learning scheme but also indicate that the proposed ConResNet is more accurate than six top-ranking methods in brain tumor segmentation and seven top-ranking methods in pancreas segmentation. Code is available at: https://***/ConResNet © 2020, CC-BY-NC-SA.

关键词： Mapping

Vehicle Re-Identification in Aerial Imagery: dataset and Approach

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Vehicle Re-Identification in Aerial Imagery: Dataset and App...

International Conference on Computer Vision (ICCV)

作者： Peng Wang Bingliang Jiao Lu Yang Yifei Yang Shizhou Zhang Wei Wei Yanning Zhang School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

In this work, we construct a large-scale dataset for vehicle re-identification (ReID), which contains 137k images of 13k vehicle instances captured by UAV-mounted cameras. To our knowledge, it is the largest UAV-based vehicle ReID dataset. To increase intra-class variation, each vehicle is captured by at least two UAVs at different locations, with diverse view-angles and flight-altitudes. We manually label a variety of vehicle attributes, including vehicle type, color, skylight, bumper, spare tire and luggage rack. Furthermore, for each vehicle image, the annotator is also required to mark the discriminative parts that helps them to distinguish this particular vehicle from others. Besides the dataset, we also design a specific vehicle ReID algorithm to make full use of the rich annotation information. It is capable of explicitly detecting discriminative parts for each specific vehicle and significantly outperforming the evaluated baselines and state-of-the-art vehicle ReID approaches.

关键词： Image color analysis Cameras Feature extraction Task analysis Image resolution Tires Visualization

Boundary-Aware Network for Abdominal Multi-Organ Segmentation

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arXiv 2022年

作者： Hu, Shishuai Liao, Zehui Xia, Yong 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

Automated abdominal multi-organ segmentation is a crucial yet challenging task in the computer-aided diagnosis of abdominal organ-related diseases. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of abdominal organs remains challenging, due to the varying sizes of abdominal organs and the ambiguous boundaries among them. In this paper, we propose a boundary-aware network (BA-Net) to segment abdominal organs on CT scans and MRI scans. This model contains a shared encoder, a boundary decoder, and a segmentation decoder. The multi-scale deep supervision strategy is adopted on both decoders, which can alleviate the issues caused by variable organ sizes. The boundary probability maps produced by the boundary decoder at each scale are used as attention to enhance the segmentation feature maps. We evaluated the BA-Net on the Abdominal Multi-Organ Segmentation (AMOS) Challenge dataset and achieved an average Dice score of 89.29% for multi-organ segmentation on CT scans and an average Dice score of 71.92% on MRI scans. The results demonstrate that BA-Net is superior to nnUNet on both segmentation tasks. © 2022, CC BY-NC-ND.

关键词： Computerized tomography

Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation

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arXiv 2021年

作者： Hu, Shishuai Liao, Zehui Zhang, Jianpeng Xia, Yong The 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

The domain gap caused mainly by variable medical image quality renders a major obstacle on the path between training a segmentation model in the lab and applying the trained model to unseen clinical data. To address this issue, domain generalization methods have been proposed, which however usually use static convolutions and are less flexible. In this paper, we propose a multi-source domain generalization model based on the domain and content adaptive convolution (DCAC) for the segmentation of medical images across different modalities. Specifically, we design the domain adaptive convolution (DAC) module and content adaptive convolution (CAC) module and incorporate both into an encoder-decoder backbone. In the DAC module, a dynamic convolutional head is conditioned on the predicted domain code of the input to make our model adapt to the unseen target domain. In the CAC module, a dynamic convolutional head is conditioned on the global image features to make our model adapt to the test image. We evaluated the DCAC model against the baseline and four state-of-the-art domain generalization methods on the prostate segmentation, COVID-19 lesion segmentation, and optic cup/optic disc segmentation tasks. Our results not only indicate that the proposed DCAC model outperforms all competing methods on each segmentation task but also demonstrate the effectiveness of the DAC and CAC modules. Code is available at https://***/DCAC. © 2021, CC BY-NC-ND.

关键词： Convolution

Label Propagation for 3D Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis

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arXiv 2022年

Carotid vessel wall segmentation is a crucial yet challenging task in the computer-aided diagnosis of atherosclerosis. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of carotid vessel wall on magnetic resonance (MR) images remains challenging, due to limited annotations and heterogeneous arteries. In this paper, we propose a semi-supervised label propagation framework to segment lumen, normal vessel walls, and atherosclerotic vessel wall on 3D MR images. By interpolating the provided annotations, we get 3D continuous labels for training 3D segmentation model. With the trained model, we generate pseudo labels for unlabeled slices to incorporate them for model training. Then we use the whole MR scans and the propagated labels to re-train the segmentation model and improve its robustness. We evaluated the label propagation framework on the CarOtid vessel wall SegMentation and atherosclerOsis diagnosiS (COSMOS) Challenge dataset and achieved a QuanM score of 83.41% on the testing dataset, which got the 1-st place on the online evaluation leaderboard. The results demonstrate the effectiveness of the proposed framework. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance

Uncertainty-oriented ensemble data visualization and exploration using variable spatial spreading

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arXiv 2020年

作者： Zhang, Mingdong Chen, Li Li, Quan Yuan, Xiaoru Yong, Junhai School of Software BNRist Tsinghua University Beijing China AI Group WeBank ShenzhenGuangdong China National Engineering Laboratory for Big Data Analysis and Application Peking University Beijing China

As an important method of handling potential uncertainties in numerical simulations, ensemble simulation has been widely applied in many disciplines. Visualization is a promising and powerful ensemble simulation analysis method. However, conventional visualization methods mainly aim at data simplification and highlighting important information based on domain expertise instead of providing a flexible data exploration and intervention mechanism. Trial-and-error procedures have to be repeatedly conducted by such approaches. To resolve this issue, we propose a new perspective of ensemble data analysis using the attribute variable dimension as the primary analysis dimension. Particularly, we propose a variable uncertainty calculation method based on variable spatial spreading. Based on this method, we design an interactive ensemble analysis framework that provides a flexible interactive exploration of the ensemble data. Particularly, the proposed spreading curve view, the region stability heat map view, and the temporal analysis view, together with the commonly used 2D map view, jointly support uncertainty distribution perception, region selection, and temporal analysis, as well as other analysis requirements. We verify our approach by analyzing a real-world ensemble simulation dataset. Feedback collected from domain experts confirms the efficacy of our framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

Boundary-Aware Network for Kidney Parsing

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arXiv 2022年

作者： Hu, Shishuai Ye, Yiwen Liao, Zehui Xia, Yong 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

Kidney structures segmentation is a crucial yet challenging task in the computer-aided diagnosis of surgery-based renal cancer. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of kidney structures on computed tomography angiography (CTA) images remains challenging, due to the variable sizes of kidney tumors and the ambiguous boundaries between kidney structures and their surroundings. In this paper, we propose a boundary-aware network (BA-Net) to segment kidneys, kidney tumors, arteries, and veins on CTA scans. This model contains a shared encoder, a boundary decoder, and a segmentation decoder. The multi-scale deep supervision strategy is adopted on both decoders, which can alleviate the issues caused by variable tumor sizes. The boundary probability maps produced by the boundary decoder at each scale are used as attention to enhance the segmentation feature maps. We evaluated the BA-Net on the Kidney PArsing (KiPA) Challenge dataset and achieved an average Dice score of 89.65% for kidney structure segmentation on CTA scans using 4-fold cross-validation. The results demonstrate the effectiveness of the BA-Net. © 2022, CC BY-NC-ND.

关键词： Tumors

Characterizing Flight Delay Profiles with a Tensor Factorization Framework

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engineering 2021年第4期7卷 465-472页

作者： Mingyuan Zhang Shenwen Chen Lijun Sun Wenbo Du Xianbin Cao School of Electronic and Information Engineering Beihang UniversityBeijing 100191China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Beijing 100191China Department of Civil Engineering and Applied Mechanics McGill UniversityMontrealQC H3A 0C3Canada

In air traffic and airport management,experience gained from past operations is crucial in designing appropriate strategies when facing a new ***,this paper uses massive spatiotemporal flight data to identify similar traffic and delay patterns,which become critical for gaining a better understanding of the aviation system and relevant ***,as the datasets imply complex dependence and higher-order interactions between space and time,retrieving significant features and patterns can be very *** this paper,we propose a probabilistic framework for highdimensional historical flight *** apply a latent class model and demonstrate the effectiveness of this framework using air traffic data from 224 airports in China during 2014–*** find that profiles of each dimension can be clearly divided into various patterns representing different regular *** prove the effectiveness of these patterns,we then create an estimation model that provides preliminary judgment on the airport delay *** outcomes of this study can help airport operators and air traffic managers better understand air traffic and delay patterns according to the experience gained from historical scenarios.

关键词： Air traffic management Flight delay Latent class model Tensor decomposition