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2013 International Conference on Mechatronic Systems and Materials Application, ICMSMA 2013

作者： Dong, Ao Shuang Lou, Bin Bin Jiang, Hui Yan Tong, Qiang Yang, Guang Ming Gao, Tian Han Yang, Ben Qiang Zhang, Li Bo Software College Northeastern University Shenyang 110819 China Key Laboratory of Medical Image Computing Northeastern University Ministry of Education Shenyang 110819 China Radioactive Branch PLA General Hospital of Shenyang Military Region Shenyang 110016 China

ISBN: (纸本)9783037858158

Traditional medical image enhancement method has some disadvantages. They can not significantly improve the medical image edge, texture and detailed information. Besides the enhancement effect is susceptible to interference noise information. This paper proposed enhancement algorithms combining bidimensional empirical mode decomposition and the wavelet edge enhancement method. The first step is using the method of bidimensional empirical mode decomposition to process medical image, achieve image information with different frequency. And then our method using wavelet transform to enhance different frequency image edge, texture information. Through the comparison of proposed method with the existing method, it has been verified the proposed method has a better effect in the detail enhancement of medical images. © (2013) Trans Tech Publications, Switzerland.

关键词： image enhancement

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A Method of Pulmonary Nodule Detection utilizing multiple Support Vector Machines

A Method of Pulmonary Nodule Detection utilizing multiple Su...

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The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

作者： Jinzhu Yang Yang Liu Dazhe Zhao Jiren Liu Key Laboratory of Medical Image Computing (Northeastern University) Ministry of Education Northeast ey Laboratory of Medical Image Computing (Northeastern University) Ministry of Education Northeaste School of Information Science & Engineering Northeastern University Shenyang China

ISBN: (纸本)9781424472352

It has been proven that early detection of pulmonary nodules is an important clinical indication for early-stage lung cancer diagnosis. Recently, support vector machines(SVMs) have been extensively used in pattern recognition. However, the application object for SVMs used for false positives(FPs) reduction when detecting lung nodules is generally based on only axial plane. In this paper, we propose a computerized system aimed at lung nodules detection in Multi-Slice Computed Tomography(MSCT) scans with multiple SVMs;it segments the lung field, extracts three sets of candidates regions with two dimensional(2D) dot-enhancement filter on three slice directions respectively, reduces the FPs with multiple SVMs, and then, integrates the classification results by using pixel analysis and region growing method. The proposed scheme is applied on two lung CT datasets. The experimental results illustrate the efficiency of the proposed method.

关键词： pulmonary nodule detection computed tomography (CT) multiple support vector machines (SVMs) false positive reduction pixel analysis

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SETTP: Style Extraction and Tunable Inference via Dual-Level Transferable Prompt Learning 27

SETTP: Style Extraction and Tunable Inference via Dual-Level...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Jin, Chunzhen Huang, Yongfeng Wang, Yaqi Cao, Peng Zaïane, Osmar Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Amii University of Alberta EdmontonAB Canada

ISBN: (纸本)9781643685489

Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources. In this work, we introduce a novel method termed Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning (SETTP) for effective style transfer in low-resource scenarios. First, SETTP learns source style-level prompts containing fundamental style characteristics from high-resource style transfer. During training, the source style-level prompts are transferred through an attention module to derive a target style-level prompt for beneficial knowledge provision in low-resource style transfer. Additionally, we propose instance-level prompts obtained by clustering the target resources based on the semantic content to reduce semantic bias. We also propose an automated evaluation approach of style similarity based on alignment with human evaluations using ChatGPT-4. Our experiments across three resourceful styles show that SETTP requires only 1/20th of the data volume to achieve performance comparable to state-of-the-art methods. In tasks involving scarce data like writing style and role style, SETTP outperforms previous methods by 16.24%. © 2024 The Authors.

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Positive-Unlabeled Learning with Field of View Consistency for Histology image Segmentation 1

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13th International Conference on Frontier computing, FC 2023

作者： Jia, Xiaoqi Fu, Chong Hou, Jiaxin Qin, Wenjian School of Computer Science and Engineering Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China

ISBN: (数字)9789819993420

ISBN: (纸本)9789819993413

Histology image annotation is costly and time-consuming. Utilizing Positive and Unlabeled (PU) data for model training offers a more resource-efficient alternative. However, previous methods for PU learning suffer from the noise arising from label assignment to unlabeled data. We observe that predictions on noisy data lack consistency under data augmentation. In this paper, we present Field of View (FoV) consistency regularization for PU segmentation in histology images, which effectively reduces the noise influence by promoting consistent predictions across varying FoVs. Using only 20% of positive labels on the Glas Dataset, our approach outperforms previous methods, achieving a Dice score of 90.69%-almost reaching the fully supervised result of 93.30%. Source code is available at: https://***/lzaya/PU_with_FoV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： image segmentation

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A Qo S Routing Scheme for Vehicle Wireless Communication Based on Improved Particle Swarm Optimization

A Qo S Routing Scheme for Vehicle Wireless Communication Bas...

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2011 International Conference on Future Wireless Networks and Information Systems(ICFWI 2011)

作者： Huilin Liu Junchang Xin Yuanyuan Yang Dapeng Wang Key Laboratory of Medical Image Computing (NEU) Ministry of Education College of Information Science and Engineering Northeastern University

The vehicle wireless communication has become the hot issue,which makes multi-constrained Qo S routing communication the focus of *** of the network topology of vehicle wireless communication changing every moment,wireless network environment complex and changing,highspeed moving network nodes,we present an improved particle swarm optimization algorithm(PSO).The improved PSO combines with the thought of genetic algorithms' crossover and mutation to solve routing problem with multi-constrained Qo *** improved algorithm makes us find the right route in a short time as possible,and when the route is off,we can find another route to realize the communication between *** experimental results show that the algorithm is *** has a better performance than the routing algorithm based on PSO or GA alone.

关键词： Vehicle wireless communication,routing,particle swarm optimization(PSO),genetic algorithm,multi-constrained QoS

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An intelligent Evaluation Method of Root Canal Therapy Quality Based on Deep Learning

An Intelligent Evaluation Method of Root Canal Therapy Quali...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Jie Peng, Gang Yan, Shiqian Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Hubei Eya Medical Investment Management Co. Ltd Wuhan430061 China

ISBN: (纸本)9781665465335

Dentists judge the quality of root canal therapy for each patient very time-consuming, and inefficient, lack of quantitative evaluation criteria, easy to cause judgment errors. At the same time, the traditional method of extracting the root canal image features based on experience is difficult to accurately extract the filling area of the root canal and the tooth area where it is located, resulting in low accuracy of root canal target segmentation, and thus affecting the accuracy of root canal treatment quality evaluation. In this paper, for the real root canal treatment images of patients, the CenterNet network was used to detect the root canal target of root canal images. Then, according to the test results, the U-Net full convolution neural network was used to segment the root canal filling region and its tooth region. Finally, the segmented images were quantitatively evaluated according to the professional evaluation index of doctors. The experimental results show that the coincidence rate between the evaluation results of this method and the evaluation results of professional doctors is 83.3 %, which can better complete the quality evaluation of root canal therapy for patients, effectively improve the work efficiency of doctors, and has reference significance for the application of artificial intelligence in medical field. © 2022 IEEE.

关键词： Patient treatment

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Research on the Separation Process Model of Pressure Swing Adsorption for CO2/N2 with Zeolite Molecular Sieve

Research on the Separation Process Model of Pressure Swing A...

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第26届中国控制与决策会议

作者： Liying Liu Tao Du Xin Fang Shuai Che Wenjun Tan SEPA Key Lab on Eco-industry Northeastern University Key Laboratory of Medical Image Computing Ministry of EducationNortheastern University

ISBN: (纸本)9781479937097

The model of pressure swing adsorption separation process for CO2 with zeolite molecular sieve is established in this paper. The pressure swing adsorption system of CO2 is simulated with mass, gas and energy balance equations. The CO2 adsorption isotherm is fitted by Dual-site Langmuir equation. The recovery rate, productive rate and energy of the separation process is designed. Finally, the model is validated by CO2 of power plant flue gas with zeolite A and zeolite A+X. The result data of experiment and model simulation shows that our model is reliable in simulating the CO2/N2 pressure swing adsorption separation process with zeolite molecular sieve.

关键词： PSA CO2 adsorption model

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Development of the measurement system of the postero-to-anterior movements of the cervical spine

Development of the measurement system of the postero-to-ante...

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2012 International Conference on Modelling, Identification and Control, ICMIC 2012

作者： Wang, Tiantian Huang, Jian Wang, Yongji Huang, Xiaolin Zhang, Jinghui Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Technology Huazhong University of Science and Technology Wuhan China Department of Rehabilitation Medicine Tongji Hospital Tongji Medical College of Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780956715715

An essential part of improving manual therapy treatment for cervical spondylosis is to objectively quantifies the relationship between the manually applied force and the resulting displacement during Postero-to-Anterior (PA) movements. In this research we developed a reliable device which could be used in a clinical setting and record four different levels force-displacement curves of PA movements automatically. The measurement objectively quantifies the force characteristic parameters of four different levels. The ranges of amplitude and frequency are listed out and a method is given for fitting applied force curves of four different levels by manual therapists. We also calculate individual's stiffness at C2 and C5 spinous process and find that cervical spine stiffness differs between individuals. This information forms the basis for future research, teaching and clinical practice that could improve manual therapy skills and design an intelligent system for cervical spinal rehabilitation to replace manual therapists. © 2012 Huazhong Univ of Sci & Tec.

关键词： Stiffness

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Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation

arXiv

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arXiv 2023年

作者： Zheng, Xu Zhu, Jinjing Liu, Yexin Cao, Zidong Fu, Chong Wang, Lin Hong Kong Northeastern University United States Dept. of Cse Hkust Hong Kong Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Neu China

The ability of scene understanding has sparked active research for panoramic image semantic segmentation. However, the performance is hampered by distortion of the equirectangular projection (ERP) and a lack of pixel-wise annotations. For this reason, some works treat the ERP and pinhole images equally and transfer knowledge from the pinhole to ERP images via unsupervised domain adaptation (UDA). However, they fail to handle the domain gaps caused by: 1) the inherent differences between camera sensors and captured scenes;2) the distinct image formats (e.g., ERP and pinhole images). In this paper, we propose a novel yet flexible dual-path UDA framework, DPPASS, taking ERP and tangent projection (TP) images as inputs. To reduce the domain gaps, we propose cross-projection and intra-projection training. The cross-projection training includes tangent-wise feature contrastive training and prediction consistency training. That is, the former formulates the features with the same projection locations as positive examples and vice versa, for the models' awareness of distortion, while the latter ensures the consistency of cross-model predictions between the ERP and TP. Moreover, adversarial intra-projection training is proposed to reduce the inherent gap, between the features of the pinhole images and those of the ERP and TP images, respectively. Importantly, the TP path can be freely removed after training, leading to no additional inference cost. Extensive experiments on two benchmarks show that our DPPASS achieves +1.06% mIoU increment than the state-of-the-art approaches. https://***/cvpr23/DPPASS. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Research of an Electromagnetic Tracking Method Based on Two Electrically-controlled Magnetic Sources

Research of an Electromagnetic Tracking Method Based on Two ...

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2015 IEEE Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2015)

作者： Min Sha Yifeng Wang Ning Ding Xiaomei Wu Zuxiang Fang Department of Electronic Engineering Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

ISBN: (纸本)9781479919819

This paper presents an electromagnetic tracking method based on two electrically-controlled magnetic *** source consists of three mutual orthogonal *** method can skillfully determine the rotation angles of the maximum magnetic flux density vector(MMFD V) generated by a magnetic source according to the Biot-Savart *** the position of the sensor can be calculated with a noniterative geometrical algorithm in real *** results demonstrate a good performance of this method.

关键词： electromagnetic tracking MMFDV fast determination algorithm finite element analysis

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