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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shao, Wei Wang, Zhe Zhang, Ziming Zhou, Qinghua Wang, Ruoyu Tan, Wenjun Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China Affiliated Zhongshan Hospital of Dalian University Oncology Department Dalian116001 China

ISBN: (纸本)9781665468190

Segmenting the pulmonary airway in CT images to obtain its anatomical structure information can help doctors better understand the patient's condition and determine the treatment plan. However, due to the blurred boundary of the pulmonary airway in CT images, it has a high degree of overlap with the surrounding pulmonary parenchyma and other tissues, making segmentation difficult. In this paper, a method based on the separation of pulmonary airway in CT images is investigated. Based on the original U-net network, using the idea of migration learning, the U-net and VGG16 network with partially similar network structure are fused to form a VGG-Unet joint neural network, which not only accelerates the model training and image segmentation efficiency, but also ensures the accuracy of image segmentation. The experimental data source used CT images acquired from the partner hospital. The algorithm in this paper was used to segment and extract the CT images of airway of lungs with an accuracy of 86.52%, and the segmentation efficiency was high and stable. © 2022 IEEE.

关键词： image segmentation

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MFFUNet: A hybrid model with cross-attention-guided multi-feature fusion for automated segmentation of organs at risk in cervical cancer brachytherapy

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Computerized medical Imaging and Graphics 2025年 124卷

作者： Yin Gu Huimin Guo Jiahao Zhang Yuhua Gao Yuexian Li Ming Cui Wei Qian He Ma College of Medicine and Biological Information Engineering Northeastern University Shenyang 110169 Liaoning China Department of Gynecological Radiotherapy Cancer Hospital of China Medical University Shenyang 110042 Liaoning China Department of Radiation Oncology Gastrointestinal and Urinary and Musculoskeletal Cancer Cancer Hospital of China Medical University Shenyang 110042 Liaoning China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang 110169 Liaoning China

Brachytherapy is a common treatment option for cervical cancer. An important step involved in brachytherapy is the delineation of organs at risk (OARs) based on computed tomography (CT) images. Automating OARs segmentation in brachytherapy has the benefit of both reducing the time and improving the quality of radiation therapy planning. This paper introduces a novel segmentation model named MFFUNet for the automatic contour delineation of OARs in cervical cancer brachytherapy. The proposed model employs a staged encoder–decoder structure, integrating the self-attention mechanism of Transformer with the CNN framework. A novel multi-features fusion (MFF) block with a cross-attention-guided feature fusion mechanism is also proposed, which efficiently extracts and cross-fuses features from multiple receptive fields, enriching the semantic information of the features and thus improving the performance of complex segmentation tasks. A private CT image dataset of 95 patients with cervical cancer undergoing brachytherapy is used to evaluate the segmentation performance of the proposed method. The OARs in the data consist of the bladder, rectum, and colon surrounding the cervix. The proposed model surpasses current mainstream OARs segmentation models in terms of segmentation accuracy. The mean Dice similarity coefficient (DSC) score of all three OARs has achieved 73.69%. Among them, the DSC score for the bladder is 92.65%, for the rectum is 66.55%, and for the colon is 61.86%. Moreover, we also conducted experiments on two common public thoracoabdominal multi-organ CT datasets. The excellent segmentation performance further demonstrates the generalization ability of our model. In conclusion, MFFUNet has demonstrated outstanding effectiveness in segmenting OARs for cervical cancer brachytherapy. By accurately delineating OARs, it enhances radiotherapy planning precision and helps reduce radiation toxicity, improving patient outcomes.

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Adaptive delay-energy balanced partial offloading strategy in Mobile Edge computing networks

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Digital Communications and Networks 2023年第6期9卷 1310-1318页

作者： Shumei Liu Yao Yu Lei Guo Phee Lep Yeoh Branka Vucetic Yonghui Li School of Computer Science and Engineering Northeastern UniversityShenyang110819China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyang110819China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Electrical and Information Engineering University of SydneySydneyNSW2006Australia

Mobile Edge computing(MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy *** this paper,we propose a flexible MECbased requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy *** address the dimensional differences between time and energy,we introduce two normalized parameters and then derive the computational overhead of processing *** from existing works,this paper considers practical variations in the user request patterns,and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay,task workload and power *** the resulting problem is non-convex,we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading *** experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes.

关键词： Mobile edge computing(MEC) Delay Energy consumption Dynamic balance Partial computation offloading

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Recent advance in non-invasive continuous blood pressure measurement system

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Recent Patents on Biomedical Engineering 2013年第3期6卷 170-178页

作者： Xu, Lisheng He, Dianning Zhao, Yue Yin, Sainan Sino-Dutch Biomedical and Information Engineering School Northeastern University Shenyang City Liaoning Province 110004 China Key Laboratory of Medical Image Computing Ministry of Education Shenyang City Liaoning Province 110004 China

Compared with the traditional method of measuring the blood pressure, the cuff-less blood pressure measuring method can detect the blood pressure continuously in a comfortable way, in order to provide an adequate basis of clinical diagnosis and has great significance to clinical and medical research. Some reported non-invasive continuous blood pressure measurements are presented in this review paper comprising illustrations and comparisons of the characteristic of the patents based on different methods, such as arterial tonometry method, vascular unloading technique method, pulse wave transit time method and others. This review also presents the characteristics and application of some related products. The challenge and the prospective of non-invasive continuous blood pressure measurement techniques are also analyzed. Finally, to conclude, non-invasive continuous blood pressure measurement is becoming the future development trend of blood pressure measurement, however, the accuracy of measurement also needs to be improved. © 2014 Bentham Science Publishers.

关键词： Blood pressure

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Detecting hot topics in Chinese microblog streams based on frequent patterns mining

Detecting hot topics in Chinese microblog streams based on f...

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2012 International Conference on Web Information Systems and Mining, WISM 2012

作者： Xu, Weili Feng, Shi Wang, Lin Wang, Daling Yu, Ge School of Information Science and Engineering Ministry of Education Northeastern University Shenyang 110819 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang 110819 China

ISBN: (纸本)9783642334689

Microblog plays a more and more important role on the emerging and propagation of the public opinion on the Web. Although topic detection has long been a hot research topic, the characteristics of microblog make it a nontrivial task. In this paper, we propose a novel hot topic detection approach based on keyword extraction and frequent patterns mining. We analyze the characteristics of hot topic microblogs and the topical keywords are extracted according to the increasing rate and frequency in Chinese microblog streams. Different from traditional clustering based detection methods, in this paper we treat the short texts of microblogs as transaction items, and apply Apriori algorithm to generate the hot topics. The experiments in the real dataset verify the efficiency and effectiveness of our proposed methods. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Extraction

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Cost sensitive adaptive random subspace ensemble for computer-aided nodule detection

Cost sensitive adaptive random subspace ensemble for compute...

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作者： Cao, Peng Zhao, Dazhe Zaiane, Osmar Key Laboratory of Medical Image Computing Ministry of Education Northeastern University China University of Alberta Edmonton AB Canada

Many lung nodule computer-aided detection methods have been proposed to help radiologists in their decision making. Because high sensitivity is essential in the candidate identification stage, there are countless false positives produced by the initial suspect nodule generation process, giving more work to radiologists. The difficulty of false positive reduction lies in the variation of the appearances of the potential nodules, and the imbalance distribution between the amount of nodule and non-nodule candidates in the dataset. To solve these challenges, we extend the random subspace method to a novel Cost Sensitive Adaptive Random Subspace ensemble (CSARS), so as to increase the diversity among the components and overcome imbalanced data classification. Experimental results show the effectiveness of the proposed method in terms of G-mean and AUC in comparison with commonly used methods. © 2013 IEEE.

关键词： Decision making

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Virtually-Federated Scheduling of Parallel Real-Time Tasks 42

Virtually-Federated Scheduling of Parallel Real-Time Tasks

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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： Jiang, Xu Guan, Nan Liang, Haochun Tang, Yue Qiao, Lei Yi, Wang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University China City University of Hong Kong Hong Kong Beijing Institute of Control Engineering China Uppsala University Sweden

ISBN: (纸本)9781665428026

Federated scheduling is a promising approach to schedule parallel real-time tasks, where each task exclusively executes on a set of dedicated processors. However, federated scheduling suffers significant resource wasting since a task typically only uses part of the processing capacity allocated to it, while the unused part cannot be shared with other tasks. To solve this problem, we present a virtually-federated scheduling approach, which both enjoys the good analyzability of federated scheduling and allows tasks to efficiently share processors with others. The main idea is to construct virtual processors on physical processors, and let a task exclusively execute on a set of virtual processors. As a physical processor is shared by virtual processors, tasks effectively share processors with each other. On the other hand, as each task exclusively executes on its own virtual processor set, the good analyzability of federated scheduling can be carried into to our virtually-federated scheduling approach. We conduct comprehensive performance evaluation to compare our proposed approach with existing methods of different types. Experiment results show that our approach consistently outperforms existing methods to a considerable extent under a wide range of parameter settings. ©2021 IEEE

关键词： Scheduling

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CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior 28th

CAB: Empathetic Dialogue Generation with Cognition, Affecti...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Gao, Pan Han, Donghong Zhou, Rui Zhang, Xuejiao Wang, Zikun School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Swinburne University of Technology Hawthorn Australia

ISBN: (纸本)9783031306747

Empathy is an important characteristic to be considered when building a more intelligent and humanized dialogue agent. However, existing methods did not fully comprehend empathy as a complex process involving three aspects: cognition, affection and behavior. In this paper, we propose CAB, a novel framework that takes a comprehensive perspective of cognition, affection and behavior to generate empathetic responses. For cognition, we build paths between critical keywords in the dialogue by leveraging external knowledge. This is because keywords in a dialogue are the core of sentences. Building the logic relationship between keywords, which is overlooked by the majority of existing works, can improve the understanding of keywords and contextual logic, thus enhance the cognitive ability. For affection, we capture the emotional dependencies with dual latent variables that contain both interlocutors’ emotions. The reason is that considering both interlocutors’ emotions simultaneously helps to learn the emotional dependencies. For behavior, we use appropriate dialogue acts to guide the dialogue generation to enhance the empathy expression. Extensive experiments demonstrate that our multi-perspective model outperforms the state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge management

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Liver vessel segmentation based on densely connected three-dimensional fully convolutional neural network 4

Liver vessel segmentation based on densely connected three-d...

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4th International Symposium on image computing and Digital Medicine, ISICDM 2020

作者： Fu, Meihan Hu, Ying Yang, Jinzhu College of Marine Electrical Engineering Dalian Maritime University Liaoning Dalian116000 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110000 China

ISBN: (纸本)9781450389686

Liver vessel segmentation is an important task of liver tumor resection and ablation. This paper proposes a liver vessel segmentation method based on an improved V-Net network. The dense connection is introduced into the network, which strengthens the feature transfer and reuses between convolutional layers, and can effectively alleviate the disappearance of the gradient of the network. The experimental results on the public data set 3Dircadb shown that the dice, accuracy, and sensitivity are 71.2%, 73.9%, and 97.8% respectively. The final experiment showed that the improved network has good generalization ability and can effectively improve the accuracy of liver vessel segmentation. © 2020 ACM.

关键词： Convolutional neural networks

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An Unsupervised Domain Adaptation Model Based on Multi-Level Joint Alignment for Multi-Modal Cardiac image Segmentation

An Unsupervised Domain Adaptation Model Based on Multi-Level...

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2023 International Conference on Biomimetic Intelligence and Robotics

作者： Li, Jiapeng Lv, Yimou Xu, Lisheng Qi, Lin College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang110169 China

Unsupervised Domain Adaptation has greatly boosted the performance of multi-modal medical segmentation when there are only source domain labels and no labels in the target domain. Many previous work relies on Convolutional Neural Networks for distribution or instance alignment, however, the receptive field of CNN makes it overly concerned with texture and local semantic information of images, while losing the global style information and semantic information and lacking relationships between local and global semantics. In order to address these issues, we propose a two-stage, multi-level framework for unsupervised domain adaptation, which consists of an image translation network and a Transformer-based domain adaptation segmentation network, jointly aligning the data distribution in the source and target domains from the image, feature and output level through adversarial learning. Experimental results indicate that our method can achieve satisfactory results and outperform other state-of-the-art medical image segmentation methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： Textures

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