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2013 IEEE International Conference on medical Imaging Physics and Engineering

作者： Quanhai Fu Chaojie Li Sino-Dutch Biomedical and Information Engineering School Northeastern University Key Laboratory of Medical Image Computing Ministry of EducationNortheastern University

Currently,dental CBCT(Cone-Beam Computed Tomography) is wildly used in the tooth implantation,maxillofacial surgery and the treatment of temporomandibular joint *** three-dimensional images of Dental CBCT are reconstructed based on the projection images,which are obtained at each degree of *** projected image should have a precise correspondence with the angle of rotation of *** vibration occurs during the rotation,artifacts will be caused from reconstruction,thus affecting the image *** has a very important significance on the image reconstruction if vibration information of CBCT arm can be detected and *** paper presented a vibration measuring device based 3D accelerometer,which can measure and evaluate vibration of CBCT during *** this device,vibration information can be sensed by 3D *** analog signals are sampled by Microprocessor with internal ADC ***,all data are sent to PC via wireless communication *** written by MATLAB will acquire,display,save and analyze data from measurement device,which makes it possible that evaluates vibration information from CBCT.

关键词： 3D accelerometer,CBCT,vibration

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A Machine Learning Approach to Biomarker Screening on Proteomics Data of Cleft Lip and Palate 2020

A Machine Learning Approach to Biomarker Screening on Proteo...

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The Fourth International Symposium on image computing and Digital Medicine

作者： Weidong Xie Linjie Wang Kun Yu Wei Li School of Computer Science and Engineering Northeastern University China College of medicine and bioinformation engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image (MIIC) Northeastern University Ministry of Education China

ISBN: (纸本)9781450389686

Genomic and proteomic techniques have provided new research ideas for the early diagnosis of diseases such as cleft lip and palate (CLP). However, a significant challenge is finding the best set of biomarkers for the disease’s clinical diagnosis in massive or high dimensions. Existing studies have focused on refinements and combinations of feature selection methods to improve classification accuracy without interpreting the results. In this paper, seven feature selection methods are firstly compared on eight publicly available genomics microarray data. Moreover, five classification models and two pre-processing methods are aslo compared to find the most appropriate method for processing the microarray data. The method is finally validated on a CLP dataset. The results indicate that the methods described herein can achieve high classification accuracy and provide better feature interpretation.

关键词： cleft lip and palate feature selection Genomics clinical interpretation

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3D Display Technology in medical Imaging Field

3D Display Technology in Medical Imaging Field

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2013 IEEE International Conference on medical Imaging Physics and Engineering

作者： Yue Zhao Yongtao Wei Xiaoyu Cui Luxuan Qu Lin Liu Yusong Wang Sino-Dutch Biomedical and Information Engineering School Northeastern University Key Laboratory of Medical Image Computing Ministry of EducationNortheastern University

3D display can reflect the image depth information and make the image present stereoscopic sensation to improve the medical imaging effect and diagnosis accuracy,if the technology can be used in image guide surgery to diagnose lesion *** paper will mainly elaborate some advanced naked eye 3D display technologies--multi-view auto-stereoscopic display technology,integral photography 3D display technology and holographic 3D display *** last,image guide surgery based on interactive 3D navigation system is ***,this paper indicates the trend of medical imaging fields.

关键词： 3D Display,medical Imaging,Diagnosis,Interactive,Navigation.

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Parallel Implementations of image Processing Algorithms on Multi-Core

Parallel Implementations of Image Processing Algorithms on M...

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International Conference on Genetic and Evolutionary computing (ICGEC)

作者： Ying Liu Fuxiang Gao Key Laboratory of Medical Image Computing Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

The broad introduction of multi-core processors into computing has brought a great opportunity to deploy computationally demanding applications such as signal and image processing on parallel computing platforms. However it is not an easy task to decompose a computational problem into sub-problems to explore the massive parallelism provided by multi-core processors. In this paper, we study the cubic convolution interpolation algorithm for image processing. We shall parallelize the algorithm using the parallel programming tools TBB and OpenMP, and compare the performance of parallel and sequential implementations. Our experiments show that the parallel implementation of the algorithm using results in a speed-up about 200% compared with sequential implementation on a Dual-core processor, while a speed-up about 400% on a Quad-core processor.

关键词： Interpolation Parallel processing Convolution Parallel programming image processing Instruction sets Pixel

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Learning bronchiole-sensitive airway segmentation cnns by feature recalibration and attention distillation 23rd

Learning bronchiole-sensitive airway segmentation cnns by fe...

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23rd International Conference on medical image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang China UdL INSA Lyon CREATIS CNRS UMR 5220 INSERM U1206 Lyon France

ISBN: (纸本)9783030597092

Training deep convolutional neural networks (CNNs) for airway segmentation is challenging due to the sparse supervisory signals caused by severe class imbalance between long, thin airways and background. In view of the intricate pattern of tree-like airways, the segmentation model should pay extra attention to the morphology and distribution characteristics of airways. We propose a CNNs-based airway segmentation method that enjoys superior sensitivity to tenuous peripheral bronchioles. We first present a feature recalibration module to make the best use of learned features. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce the airway-specific representation learning. High-resolution attention maps with fine airway details are passing down from late layers to previous layers iteratively to enrich context knowledge. Extensive experiments demonstrate considerable performance gain brought by the two proposed modules. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. © Springer Nature Switzerland AG 2020.

关键词： Distillation

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An approach of sentiment lexicon building and orientation analysis for chinese microblogs

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Journal of Computational Information Systems 2012年第3期8卷 1113-1120页

作者： Wang, Lin Feng, Shi Song, Kaisong Wang, Daling School of Information science and Engineering Northeastern University Shenyang 110819 China Key Laboratory of Medical Image Computing Northeastern University Ministry of Education Shenyang 110819 China

As a new information media, microblog has more innovative features than traditional user-generated content. Straightforward emotion expression is one of the main characteristics which means users prefer to utilize emoticons, interjections to express their sentiments. However, there is a lack of study on such modifiers and related processing methods in existing sentiment lexicons. For solving this problem, we analyze and complement the content of the existing lexicons and knowledge bases for building a brand new sentiment lexicon with above words, and propose an algorithm for sentiment orientation analysis based on the new lexicon. Moreover, we develop a system of sentiment analysis. Experiments show the proposed lexicon building method can effectively improve the performance of microblog sentiment analysis, and the prototype system has implemented the functions from data download to result output. 1553-9105/Copyright © 2012 Binary Information Press.

关键词： Sentiment analysis

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Web assistance based Chinese-English organization name translation

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Journal of Computational Information Systems 2010年第5期6卷 1483-1494页

作者： Ren, Feiliang Zhu, Jingbo Wang, Huizhen NLP Lab College of Information Science and Engineering Northeastern University Shenyang 110819 China Key Laboratory of Medical Image Computing Northeastern University Shenyang Liaoning China

In this paper, we propose a web assistant based approach to translate organization name from Chinese to English. Firstly, all the OOV terms in the input are recognized by a CRFs model. And the input is then partially translated without considering these recognized OOV terms. Secondly, we construct some efficient queries to help search engines find some web pages that contain both the original input and its correct translation. We construct queries from different aspects and correlative words information is used during query construction. At last, a similarity matching and limited expansion based translation identification approach is proposed to identify the correct translation from the returned web pages. Experimental results show that our method can handle Chinese-English organization name translation well and can improve performance of organization name translation substantially. Copyright © 2010 Binary Information Press May, 2010.

关键词： Websites

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Context-specific miRNA regulation network predicts cancer prognosis

Context-specific miRNA regulation network predicts cancer pr...

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5th IEEE International Conference on Systems Biology, ISB 2011

作者： Zhou, Xionghui Liu, Juan Liu, Changning Rayner, Simon Liang, Fengji Ju, Jingfang Li, Yinghui Chen, Shanguang Xiong, Jianghui School of Computer Science Wuhan University Wuhan China Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Virology Wuhan Institute of Virology Chinese Academy of Sciences Wuhan China Department of Pathology Stony Brook University Medical Center NY United States

ISBN: (纸本)9781457716669

MicroRNAs can regulate hundreds of target genes and play a pivotal role in a broad range of biological process. However, relatively little is known about how these highly connected miRNAs-target networks are remodelled in the context of various diseases. Here we examine the dynamic alteration of context-specific miRNA regulation to determine whether modified microRNAs regulation on specific biological processes is a useful information source for predicting cancer prognosis. A new concept, Context-specific miRNA activity (CoMi activity) is introduced to describe the statistical difference between the expression level of a miRNA's target genes and non-targets genes within a given gene set (context). The microarray gene expression profile of brain tumors from 356 patients (The Cancer Genome Atlas dataset) was converted into a CoMi activity pattern, and showed significant positive correlation with the corresponding miRNA expression pattern. In a breast cancer cohort, the differential CoMi activity between good prognosis (longer survival) vs. bad prognosis patients forms a scale-free network, which highlighted a group of important cancer-related microRNAs and GO terms, e.g. hsa-miR-34a and 'cell adhesion'. Then two breast cancer cohorts were used in outcome prediction in an independent test. Using a popular T-test feature selection method and a support vector machine (SVM) classifier with 10-fold cross-validation, the CoMi activity feature achieves an area under curve (AUC) of 0.7155, better than the AUC value of 0.6339 for feature selection based on mRNA expression. In an independent test, CoMi feature selection achieved an AUC of 0.6874. Survival analysis also shows signatures defined by CoMi activity was predictive of survival and superior to mRNAs signatures. In short, we have demonstrated the first interrogation of dynamic remodeling of context specific miRNAs regulation networks in cancer. The altered microRNAs regulation on specific contexts could be used to predi

关键词： Diseases

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Time course and neural network for comparing written and spoken words: A MEG and DTI study

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17th International Conference on Biomagnetism Advances in Biomagnetism, Biomag2010

作者： Meng, Lu Xiang, Jing Rose, Douglas Kotecha, Rupesh Vannest, Jennifer Byars, Anna Degrauw, Ton MEG Center Cincinnati Children's Hospital Medical Center 3333 Burnet Avenue Cincinnati OH 45220 United States Key Laboratory of Medical Image Computing of Ministry of Education Northeast University Shenyang 110004 China Department of Neurology Cincinnati Children's Hospital Medical Center 3333 Burnet Avenue Cincinnati OH 45220 United States

ISBN: (纸本)9783642121968

The present study was designed to investigate the spatiotemporal signature as well as the neural network of brain activation associated with word recognition. Twenty participants were studied with a whole head magnetoencephalography (MEG) system and a 3T magnetic resonance imaging (MRI) scanner. The word stimuli consisted of 100 matched words (the visually and acoustically presented words were the same) and 20 mismatched words (the visually and acoustically presented words were different). The time course of brain activation was analyzed with a virtual sensor technique at source levels. The spatial location and connection were estimated with wavelet-based beamformer and diffusion tensor imaging (DTI) tractography. To our knowledge, this is the first study focusing on the time course and neural substrates involved in comparing spoken and written words with both MEG and DTI. The visually and acoustically presented words evoked three responses named as PM1, PM2 and PM3, respectively. The results of magnetic source imaging showed that the PM1 was localized to the primary auditory and visual cortices. The PM2 and PM3 were localized to Wernicke's area and Broca's area. Interestingly, virtual sensor waveforms showed a clear response between PM1 and PM2 from the left temporal-occipital junction. This component was consistently identified around 162 ± 8.9 ms (M160) in 20 participants (100%, 20/20). MEG source-guided DTI tractography revealed neural fibers in this junction area. The results suggested that the M160 was one part of the neural network of auditory and visual word recognition. The results of the present study yield highly convincing evidence that the left temporal-occipital junction plays an important role in comparing visually and acoustically presented words. © Springer-Verlag 2010.

关键词： Magnetic resonance imaging

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A bi-level formulation for label noise learning with spectral cluster discovery 29

A bi-level formulation for label noise learning with spectra...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Luo, Yijing Han, Bo Gong, Chen PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Lab of Image Video Understanding for Social Security China Department of Computer Science Hong Kong Baptist University Hong Kong Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9780999241165

Practically, we often face the dilemma that some of the examples for training a classifier are incorrectly labeled due to various subjective and objective factors. Although intensive efforts have been put to design classifiers that are robust to label noise, most of the previous methods have not fully utilized data distribution information. To address this issue, this paper introduces a bi-level learning paradigm termed "Spectral Cluster Discovery" (SCD) for combating with noisy labels. Namely, we simultaneously learn a robust classifier (Learning stage) by discovering the low-rank approximation to the ground-truth label matrix and learn an ideal affinity graph (Clustering stage). Specifically, we use the learned classifier to assign the examples with similar label to a mutual cluster. Based on the cluster membership, we use the learned affinity graph to explore the noisy examples based on the cluster membership. Both stages will reinforce each other iteratively. Experimental results on typical benchmark and real-world datasets verify the superiority of SCD to other label noise learning methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Iterative methods

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