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arXiv 2022年

作者： Pham, Huy Hieu Nguyen, Ha Q. Nguyen, Hieu T. Le, Linh T. Khanh, Lam College of Engineering and Computer Science VinUniversity Hanoi Viet Nam VinUni-Illinois Smart Health Center VinUniversity Hanoi Viet Nam Smart Health Center VinBigData JSC Hanoi Viet Nam Vingroup Big Data Institute Hanoi Viet Nam Hanoi Medical University Hospital Department of Radiology Hanoi Viet Nam Hospital 108 Department of Radiology Hanoi Viet Nam

Interpretation of chest radiographs (CXR) is a difficult but essential task for detecting thoracic abnormalities. Recent artificial intelligence (AI) algorithms have achieved radiologist-level performance on various medical classification tasks. However, only a few studies addressed the localization of abnormal findings from CXR scans, which is essential in explaining the image-level classification to radiologists. Additionally, the actual impact of AI algorithms on the diagnostic performance of radiologists in clinical practice remains relatively unclear. To bridge these gaps, we developed an explainable deep learning system called VinDr-CXR that can classify a CXR scan into multiple thoracic diseases and, at the same time, localize most types of critical findings on the image. VinDr-CXR was trained on 51,485 CXR scans with radiologist-provided bounding box annotations. It demonstrated a comparable performance to experienced radiologists in classifying 6 common thoracic diseases on a retrospective validation set of 3,000 CXR scans, with a mean area under the receiver operating characteristic curve (AUROC) of 0.967 (95% confidence interval [CI]: 0.958–0.975). The VinDr-CXR was also externally validated in independent patient cohorts and showed its robustness. For the localization task with 14 types of lesions, our free-response receiver operating characteristic (FROC) analysis showed that the VinDr-CXR achieved a sensitivity of 80.2% at the rate of 1.0 false-positive lesion identified per scan. A prospective study was also conducted to measure the clinical impact of the VinDr-CXR in assisting six experienced radiologists. The results indicated that the proposed system, when used as a diagnosis supporting tool, significantly improved the agreement between radiologists themselves with an increase of 1.5% in mean Fleiss’ Kappa. We also observed that, after the radiologists consulted VinDr-CXR’s suggestions, the agreement between each of them and the system was remarkab

关键词： Object detection

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Research on Paper Intelligent Plagiarism Detection Method Based on Idea Tendency 19

Research on Paper Intelligent Plagiarism Detection Method Ba...

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Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence

作者： Gang Liu Hanmo Zhang Wangyang Liu Yang Cao Weiping Fu College of Computer Science and Technology Harbin Engineering University Harbin China CETC big data research institute company limited GuiYang China

ISBN: (纸本)9781450372619

This paper proposes a method that analyzes the tendency of the paper for the originality detection of papers. This method is aiming at the behavior of idea plagiarism. The plagiarism detection task can be divided into three steps which are retrieval of candidate source document set, selection of source document set and post processing. The texts are modeled by the topic model and then are divided into several topic blocks by combining the topic modeling with the classical text segmentation method TextTiling. Then, the topic blocks which may be the source block of plagiarism are filtered out by calculating topic similarities among these topic fragments. On this basis, topic words of the topic block to be measured are extracted and its idea tendency aiming at each topic word are analyzed by the sentiment analysis method based on the dictionary. And further the source topic block set is filtered out based on the idea tendency Finally, a method of evaluating the original degree of the text is given for the intelligent plagiarism detection by the fusion of topic similarity and the result of the analysis of idea tendencies. Through experimental verification, this method can effectively evaluate the originality of the paper and has a good accuracy.

关键词： Idea Tendency

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The DUDFTO Attack: Towards Down-to-UP Timeout Probing and Dynamically Flow Table Overflowing in SDN

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IEEE Transactions on Network and Service Management 2025年

作者： Li, Jiasong Cui, Yunhe Chen, Yi Shen, Guowei Guo, Chun Qian, Qing Guizhou University State Key Laboratory of Public Big Data Engineering Research Center of Text Computing and Cognitive Intelligence Ministry of Education College of Computer Science and Technology Guizhou Provincial Characteristic Key Laboratory of Software Engineering and Information Security Guiyang550000 China Guizhou University of Finance and Economics School of Information Guiyang550000 China

As a new network structure, the decoupling of the control plane and forwarding plane makes software-Defined Networking (SDN) widely used in large-scale network scenarios. However, the decoupling network architecture also brings new vulnerabilities. The flow table overflow attack is an attack strategy that can overwhelm SDN switches. Nevertheless, the existing flow table overflow attacks may fail in probing timeouts and match fields of flow entries, due to link failure, measurement of the round-trip time (RTT) of different packets, interference of hard-timeout and idle-timeout. Meanwhile, the stealthiness of the existing attacks may also reduce, as these attacks use fixed attack rate. To improve the timeout probing accuracy and the stealthiness of attack, a new flow table overflow attack strategy, DUDFTO, is proposed to accurately probe timeout settings and match fields, then stealthily overflow SDN flow tables. Firstly, it probes the match fields by measuring the one-sided transmission delay of the packets. After that, DUDFTO designs a down-to-up feedback-based timeout probing algorithm to eliminate the issues caused by high RTT, link failure, interference between hard-timeout and idle-timeout. Then, DUDFTO designs a dynamic attack packets sending algorithm to improve its stealthiness. Finally, DUDFTO probes the flow table state to stop sending new attack packets. The evaluation results demonstrate that DUDFTO outperforms the existing attacks in terms of match fields probing ability, timeout probing relative error, number of packet_in and flow_mod messages generated by the attack, rate distribution of packet_in and flow_mod messages generated during the attack, and number of detected attack packets. © 2004-2012 IEEE.

关键词： flow table overflow attack information probing SDN switchs

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Research on paper intelligent plagiarism detection method based on idea tendency 2

Research on paper intelligent plagiarism detection method ba...

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2nd International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2019

作者： Liu, Gang Zhang, Hanmo Liu, Wangyang Cao, Yang Fu, Weiping College of Computer Science and Technology Harbin Engineering University Harbin China CETC Big Data Research Institute Company Limited GuiYang China

ISBN: (纸本)9781450372619

关键词： Sentiment analysis

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Ensemble Learning Based Sleeping Cell Detection in Cloud Radio Access Networks

Ensemble Learning Based Sleeping Cell Detection in Cloud Rad...

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IEEE Symposium on computers and Communications (ISCC)

作者： Zhao Ming Chuan Sun Xiuhua Li Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728180861

ISBN: (纸本)9781728180878

Sleeping cell problem refers to the degradation or unavailability of network services without triggered alarm, which is one of the most critical issues in current mobile networks. This problem is generally not detectable by the operators but only revealed after users' complaints occur. Therefore, it leads to the degradations of network performance in the service provision in the long run. To address this problem, we introduce a cloud-based sleeping cell detection platform into radio access networks (RANs) to detect the sleeping cells and deal with them automatically. In the cloud RANs (C-RANs), we combine and improve different methods employed in the pioneering studies in this field, and creatively use labeled training data and ensemble learning method for improving the accuracy. Particularly, we utilize expert optimization experience for further improving the detection framework. To evaluate the proposed ensemble learning based sleeping cell detection framework, we use a time-series dataset of Key Performance Indicator (KPI) in a real-world network. Trace-driven evaluation results show that the proposed framework can achieve up to 14.38% and 20.50% improvements compared with two existing schemes, respectively.

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VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs

arXiv

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arXiv 2021年

作者： Nguyen, Hieu T. Pham, Hieu H. Nguyen, Nghia T. Nguyen, Ha Q. Huynh, Thang Q. Dao, Minh Vu, Van Medical Imaging Center Vingroup Big Data Institute Hanoi Viet Nam School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Viet Nam College of Engineering & Computer Science VinUniversity Hanoi Viet Nam Department of Mathematics Yale University New Heaven United States

Radiographs are used as the most important imaging tool for identifying spine anomalies in clinical practice. The evaluation of spinal bone lesions, however, is a challenging task for radiologists. This work aims at developing and evaluating a deep learning-based framework, named VinDr-SpineXR, for the classification and localization of abnormalities from spine X-rays. First, we build a large dataset, comprising 10,468 spine X-ray images from 5,000 studies, each of which is manually annotated by an experienced radiologist with bounding boxes around abnormal findings in 13 categories. Using this dataset, we then train a deep learning classifier to determine whether a spine scan is abnormal and a detector to localize 7 crucial findings amongst the total 13. The VinDr-SpineXR is evaluated on a test set of 2,078 images from 1,000 studies, which is kept separate from the training set. It demonstrates an area under the receiver operating characteristic curve (AUROC) of 88.61% (95% CI 87.19%, 90.02%) for the image-level classification task and a mean average precision (mAP@0.5) of 33.56% for the lesion-level localization task. These results serve as a proof of concept and set a baseline for future research in this direction. To encourage advances, the dataset, codes, and trained deep learning models are made publicly available. © 2021, CC BY.

关键词： Classification (of information)

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High-Resolution Natural Image Matting by Refining Low-resolution Alpha Mattes

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 PP卷 PP页

作者： Xianmin Ye Yihui Liang Mian Tan Fujian Feng Lin Wang Han Huang College of Data Science and Information Engineering Guizhou Minzu University Guiyang China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Key Laboratory of Pattern Recognition and Intelligent System and the College of Data Science and Information Engineering Guizhou Minzu University Guiyang China School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot (SCUT) MOE of China Guangzhou China Guangdong Engineering Center for Large Model and GenAI Technology Guangzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

High-resolution natural image matting plays an important role in image editing, film-making and remote sensing due to its ability of accurately extract the foreground from a natural background. However, due to the complexity brought about by the proliferation of resolution, the existing image matting methods cannot obtain high-quality alpha mattes on high-resolution images in reasonable time. To overcome this challenge, we introduce a high-resolution image matting framework based on alpha matte refinement from low-resolution to high-resolution (HRIMF-AMR). The proposed framework transforms the complex high-resolution image matting problem into low-resolution image matting problem and high-resolution alpha matte refinement problem. While the first problem is solved by adopting an existing image matting method, the latter is addressed by applying the Detail Difference Feature Extractor (DDFE) designed as a part of our work. The DDFE extracts detail difference features from high-resolution images by measuring the image feature difference between high-resolution images and low-resolution images. The low-resolution alpha matte is refined according to the extracted detail difference feature, providing the high-resolution alpha matte. In addition, the Matte Detail Resolution Difference (MDRD) loss is introduced to train the DDFE, which imposes an additional constraint on the extraction of detail difference features with mattes. Experimental results show that integrating HRIMF-AMR significantly enhances the performance of existing matting methods on high-resolution images of Transparent-460 and Alphamatting. Project page: https://***/yexianmin/HRAMR-Matting.

关键词： Feature extraction Image resolution Image color analysis Optimization Remote sensing Clustering algorithms Accuracy Visualization Transforms Training

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Dual-domain decoupled fusion network for semantic segmentation of remote sensing images

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Information Fusion 2025年 124卷

作者： Xin Li Feng Xu Jue Zhang Anzhu Yu Xin Lyu Hongmin Gao Jun Zhou College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Engineering and Built Environment Griffith University Nathan Australia School of Surveying and Mapping Information Engineering University Zhengzhou China School of Information and Communication Technology Griffith University Nathan Australia

Semantic segmentation of remote sensing images (RSIs) is a challenging task due to the complexity of spatial structures, diverse object scales, and heterogeneous land-cover patterns. Traditional approaches often struggle to effectively balance fine-grained boundary details and global contextual understanding, especially for high-resolution images. In this paper, we propose DDFNet, a novel dual-domain decoupled fusion network, to address these challenges. DDFNet integrates spatial and frequency domain features through a dynamic decoupling and fusion strategy. Specifically, we introduce a Dual-Domain Decoupled Feature Fusion (DDFF) module that selectively combines high- and low-frequency components from both domains, enabling the model to capture local textures and global semantics. To further enhance segmentation accuracy, we design a High-Order Geometric Prior Generation (HGPG) module, which utilizes gradient and curvature information to improve boundary precision and maintain geometric consistency. Extensive experiments on three benchmark datasets — ISPRS Vaihingen, ISPRS Potsdam, and LoveDA — demonstrate that DDFNet achieves state-of-the-art performance. Ablation studies validate the contributions of the DDFF and HGPG modules, and efficiency analysis shows DDFNet’s strong adaptability to different computational constraints.

关键词： Attention mechanism Feature fusion Remote sensing images Semantic segmentation

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Shared Word Embedding Space Modeling Method Based on Orthogonal Projection 2019

Shared Word Embedding Space Modeling Method Based on Orthogo...

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Proceedings of the 3rd International Conference on Vision, Image and Signal Processing

作者： Gang Liu Kai Wang Wangyang Liu Yang Cao Guang Li Harbin Engineering University College of Computer Science and Technology Heilongjiang Harbin China CETC big data research institute company limited Guiyang China

ISBN: (纸本)9781450376259

With the continuous development of computer technology, machine learning has been applied in more and more fields. However, the application of word embedding technology in bilingual Chinese and English still needs to be developed. In this paper, we propose a model construction process based on orthogonal projection, and analyze the validity of the model from multiple perspectives. We carry out word sense similarity experiments and word analogy experiments for the quality of single language in the model, and cross-language text similarity experiments for different linguistic quality in the model. Through the analysis of the experimental results, it can be proved that the proposed shared word embedding space model achieves good results compared with the traditional word embedding model, and the effect of the model achieves the desired purpose.

关键词： Cross Language Document Similarity Shared Word Embedding Technology Orthogonal Projection Machine Learning

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BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images

arXiv

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arXiv 2022年

作者： Cong, Runmin Yang, Haowei Jiang, Qiuping Gao, Wei Li, Haisheng Wang, Cong Zhao, Yao Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China The Department of Computer Science City University of Hong Kong Hong Kong The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The School of Information Science and Engineering Ningbo University Ningbo315211 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China The Distributed and Parallel Software Lab Huawei Technologies Shenzhen518129 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this paper, we propose a novel network for automatic COVID-19 lung infection segmentation from CT images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage that includes three progressively Boundary-Context-Semantic Reconstruction (BCSR) blocks. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder by highlighting the important spatial and boundary locations and modeling the global context dependence. Besides, a semantic guidance (SG) unit generates the semantic guidance map to refine the decoder features by aggregating multi-scale high-level features at the intermediate resolution. Extensive experiments demonstrate that our proposed framework outperforms the existing competitors both qualitatively and quantitatively. © 2022, CC BY-NC-SA.

关键词： COVID-19

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