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Finite Element Analysis for the Effects of the Descending Aorta Tortuosity on Aortic Hemodynamics

Procedia Computer Science 2022年 209卷 148-156页

作者： Jiapeng Li Xuehao Cao Lisheng Xu Lin Qi College of Medicine and Biological Information Engineering Northeastern University Shenyang 110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang 110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang 110169 China

Large arteries in the human body, such as the aorta, are susceptible to atherosclerosis, and aortic hemodynamic analysis has been widely used to predict cardiovascular disease. In this study, we aim to explore whether the tortuosity of the descending aorta (DA) is associated with the development of atherosclerosis and to investigate how vessel curvature affects blood flow characteristics. To investigate the relationship between tortuosity of DA and atherosclerosis, six models and different degrees of tortuosity were considered and combined for a total of 36 computational models. The models were numerically calculated by finite element analysis based on Fluid-Structure Interaction (FSI), and hemodynamic parameters including blood flow velocity, Wall Shear Stress (WSS), Oscillatory Shear Index (OSI), and Relative Residence Time (RRT), were calculated that are used to identify the locations of thrombosis and atherosclerosis. Our results showed that flow disturbances such as flow recirculation and secondary flow were more severe in the high-tortuosity vessels compared with the less-tortuosity vessels.

关键词： Computational Fluid Dynamics Fluid-Structure Interaction Descending Aorta Tortuosity

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A Segmentation Method of Lung Parenchyma From Chest CT images Based on Dual U-Net

A Segmentation Method of Lung Parenchyma From Chest CT Image...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Wenjun Tan Jinzhu Yang Yao Liu Xiaoxia Yin Huangying Liu Yanchun Zhang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Cyberspace Institute of Advanced Technology Guangzhou University Centre for Applied Informatics College of Engineering and Science Victoria University

ISBN: (纸本)9781728118680

Segmentation of lung parenchyma as a pretreatment step for the detection of some lung lesions requires high accuracy to provide more reliable information for lesion detection. In this paper, we proposed a segmentation method of lung parenchyma from chest CT images by Dual U-Net algorithm based on the characteristics of different lung region. Firstly, the original lung region is extracted from the full chest CT images of patient by an automatic threshold selection method. Then, the images of the different regions of lung is obtained by calculating the area. Finally, the Dual U-Net is trained according the different lung regions. The first U-Net algorithm operates these middle chest images and other images are operated by the second U-Net algorithm to reduce the interference of other images. The experimental results shown that the segmenting results of lung parenchyma by this method are more accurate than the results by single U-Net and U-Net++ algorithm.

关键词： Lung parenchyma segmentation U-Net chest CT images Lung Tissue Chest imaging chest ct images Lung Dual lung lesion

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Transwnet: Integrating Transformers into CNNS via Row and Column Attention for Abdominal Multi-Organ Segmentation 48

Transwnet: Integrating Transformers into CNNS via Row and Co...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xie, Yazhen Huang, Yanglin Zhang, Yuan Li, Xuanya Ye, Xiongjun Hu, Kai Xiangtan University Key Laboratory of Intelligent Computing Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China Chinese Academy of Medical Sciences and Peking Union Medical College National Cancer Center Cancer Hospital Beijing100021 China

ISBN: (纸本)9781728163277

Learning how to model global relationships and extract local details is crucial in improving the performance of multi-organ segmentation. Most existing U-shaped structure methods use feature fusion to address these two challenges, but still lack the ability to balance capturing global relationships and local details. To address these issues, we propose a novel multi-organ segmentation framework called TransWnet to mine global relationships and local details from both intra-and inter-scale perspectives. To achieve this, we innovatively design a Row and Column Swin Transformer (RCST) module that can efficiently capture global contextual features and construct local information. Specifically, we design a parallel structure of Row and Column Attention to model the global relationships of multi-scale encoded features, and further mine local information from the global relationships through a local window mechanism. Extensive experiments on the Synapse dataset show that our method outperforms state-of-The-Art approaches and achieves accurate segmentation of abdominal multi-organs. © 2023 IEEE.

关键词： Computerized tomography

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A Hybrid Feature Selection Method Based on Binary Differential Evolution and Feature Subset Correlation for Microarray Data*

A Hybrid Feature Selection Method Based on Binary Differenti...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Weidong Xie Wei Li Yushan Fang Yuhuan Chi Kun Yu Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang China Medicine and Bioinformation Engineering Northeastern University Shenyang China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Obtaining essential genes from microarray data that can diagnose diseases can be very useful for researchers to understand diseases and develop drugs. However, the high computational cost due to the “curse of dimensionality” and the high redundancy among features limit the application of evolutionary algorithms to the feature selection problem for high-dimensional data. This paper proposes a two-stage hybrid feature selection method to address this problem. In the first stage, a simple and efficient filtering me thod is used to initially filter redundant features, reduce the feature dimensionality, and reduce the search space of the evolutionary algorithm in the second stage. In the second stage, we propose an improved differential evolution algorithm. We redesign the binary quantization of the differential evolution algorithm for the characteristics of microarray data and improve the algorithm’s variation process to balance the algorithm’s search efficiency and accuracy. In addition, we define the redundancy of feature subsets and add it to the fitness function to reduce the redundancy of the final feature subsets. The proposed method is compared with classical feature selection methods and advanced hybrid feature selection methods on eight publicly available microarray data, and the effectiveness and advancement of the proposed method are demonstrated.

关键词： Drugs Correlation coefficient Quantization (signal) Filtering Redundancy Evolutionary computation Filtering algorithms

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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Research on preprocessing algorithm for PET-CT image registration

Research on preprocessing algorithm for PET-CT image registr...

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2012 3rd International Conference on Digital Manufacturing and Automation, ICDMA 2012

作者： Han, Fangfang Yang, Jinzhu Liu, Yang Zhao, Hong Sino-Dutch Biomedical and Information Engineer School Northeastern University Shenyang Liaoning 110179 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang Liaoning 110179 China Software Center Northeastern University Shenyang Liaoning 110179 China

ISBN: (纸本)9780769547725

PET and CT image registration is an important tool of clinical diagnosis of diseases. For PET and CT images, a preprocessing algorithm of medical image registration is proposed in this paper. The algorithm process includes image normalization, CT image adaptive threshold adjustment and automatic extraction of tissues based on morphology, edge detection and statistical analysis theory, and improved PET image interpolation based on physical spacing of pixels. The experiment results indicate that the preprocessing methods proposed in this paper have a great effect on PET and CT image registration research because of the exclusion of redundant information and the improvement of the registration algorithm efficiency. © 2012 IEEE.

关键词： image registration

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Electrochemical protein biosensors for disease marker detection:progress and opportunities

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Microsystems & Nanoengineering 2024年第3期10卷 1-20页

作者： Lanpeng Guo Yunong Zhao Qing Huang Jing Huang Yanbing Tao Jianjun Chen Hua-Yao Li Huan Liu School of Integrated Circuits Wuhan National Laboratory for OptoelectronicsOptics Valley LaboratoryHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Integrated CircuitsAnhui UniversityHefei 230601China School of Optoelectronic Materials and Technology Jianghan UniversityWuhan 430056China Department of Otorhinolaryngology Union HospitalTongji Medical CollegeHuazhong University of Science and Technology1277 Jiefang AvenueWuhan 430022China Wenzhou Institute of Advanced Manufacturing Technology Huazhong University of Science and TechnologyWenzhou 325000China

The development of artificial intelligence-enabled medical health care has created both opportunities and challenges for next-generation biosensor *** are extensively used as biological macromolecular markers in disease diagnosis and the analysis of therapeutic *** protein biosensors have achieved desirable specificity by using the specific antibody–antigen binding principle in ***,the active centers of protein biomarkers are surrounded by a peptide matrix,which hinders charge transfer and results in insufficient sensor ***,electrode-modified materials and transducer devices have been designed to increase the sensitivity and improve the practical application prospects of electrochemical protein *** this review,we summarize recent reports of electrochemical biosensors for protein biomarker *** highlight the latest research on electrochemical protein biosensors for the detection of cancer,viral infectious diseases,inflammation,and other *** corresponding sensitive materials,transducer structures,and detection principles associated with such biosensors are also addressed ***,we present an outlook on the use of electrochemical protein biosensors for disease marker detection for the next few years.

关键词： diagnosis specificity Electrochemical

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Correction to: Enhancing cyber defense strategies with discrete multi-dimensional Z-numbers: a multi-attribute decision-making approach

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Complex & intelligent Systems 2025年第7期11卷 1-1页

作者： Yao, Aiting Huang, Chen Zhang, Weiqi Dong, Chengzu Lu, Meiqu Mao, Junjun Liu, Xiao Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China AI Techplus Pty Ltd Organization Melbourne Australia School of Information Technology Deakin University Geelong Australia School of Data Science Lingnan University Hongkong China School of Artificial Intelligence Guangxi Minzu University Nanning China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

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Blind source separation with evolution based KICA

Blind source separation with evolution based KICA

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Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast)

作者： Peng Wu Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9781479979684

Kernel independent component analysis (KICA) has been widely used in the field of blind source separation. The selection of kernel function and its parameters plays an important role in KICA algorithm performance. An optimal kernel model should be rich enough to well map the given samples. However, users usually use a singular kernel based model in their experiments, which leads to a suboptimal kernel model. In order to solve this problem, we propose the evolution based multiple kernel independent component analysis (EMKICA), in which a convex combination of multiple base kernels is used instead of single kernel of KICA. The combination weights are learned by particle swarm optimization algorithm. Firstly, we elaborate the basic theory of KICA and concept of EMKICA, also the combination form of the composition kernel used in EMKICA. Secondly, we describe the presentation of the individuals in the particle swarm optimization algorithm, the settings of the evaluation function and general algorithm. Finally, we evaluate the separation ability of EMKICA on three different data sets including one-dimensional mixed signals, composite images and images with reflection. The experimental results verify the effectiveness of EMKICA.

关键词： Kernel Independent component analysis Algorithm design and analysis Multimedia communication Correlation Optimization Asia

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Label Correlation Guided Feature Selection for Multi-label Learning 9th

Label Correlation Guided Feature Selection for Multi-label ...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Kai Liang, Wei Cao, Peng Yang, Jinzhu Li, Weiping Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China School of Software and Microelectronics Peking University Beijing China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466731

Multi-label learning has received much attention due to its wide range of application domains. Multi-label data often has high-dimensional features, which brings more challenges to classification algorithms. Feature selection based on sparse learning is one of the most important approaches, which can select discriminative features to alleviate the curse of dimensionality. However, most of these methods do not consider feature redundancy and label correlation simultaneously. In this work, we propose a multi-label feature selection method that takes feature redundancy and label correlation into account. Specifically, the proposed method first divides features into groups according to feature correlation and then encourages competition within each group but relaxes competition between groups to eliminate redundant and irrelevant features. Moreover, we propose an approach to capture label correlation and exploit it to improve the feature selection. Finally, an iterative optimization algorithm is designed to obtain the feature weights for multi-label feature selection. Extensive experiments on various multi-label datasets demonstrate the superiority of the proposed method compared with some state-of-the-art multi-label feature selection methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature Selection

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