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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yejiang Wang Yuhai Zhao Zhengkui Wang Ling Li School of Computer Science and Engineering Northeastern University China and Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University China InfoComm Technology Cluster Singapore Institute of Technology Singapore

Self-supervised learning on graphs aims to learn graph representations in an unsupervised manner. While graph contrastive learning (GCL - relying on graph augmentation for creating perturbation views of anchor graphs and maximizing/minimizing similarity for positive/negative pairs) is a popular self-supervised method, it faces challenges in finding label-invariant augmented graphs and determining the exact extent of similarity between sample pairs to be achieved. In this work, we propose an alternative self-supervised solution that (i) goes beyond the label invariance assumption without distinguishing between positive/negative samples, (ii) can calibrate the encoder for preserving not only the structural information inside the graph, but the matching information between different graphs, (iii) learns isometric embeddings that preserve the distance between graphs, a by-product of our objective. Motivated by optimal transport theory, this scheme relies on an observation that the optimal transport plans between node representations at the output space, which measure the matching probability between two distributions, should be consistent with the plans between the corresponding graphs at the input space. The experimental findings include: (i) The plan alignment strategy significantly outperforms the counterpart using the transport distance; (ii) The proposed model shows superior performance using only node attributes as calibration signals, without relying on edge information; (iii) Our model maintains robust results even under high perturbation rates; (iv) Extensive experiments on various benchmarks validate the effectiveness of the proposed method.

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An Automatic Extraction Method of Coronary Arteries Centerline Based on Cellular Automata

An Automatic Extraction Method of Coronary Arteries Centerli...

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作者： Guangkun Ma Jinzhu Yang Hong Zhao School of Computer Science and Engineering Northeastern University School of Software Shenyang University of Technology Key Laboratory of Intelligent Computing in Medical Image Ministry of Education School of Computer Science and Engineering Northeastern University

Effectively obtaining the centerline of the coronary arteries is very important to the clinical medical diagnosis. Cellular automata are extensively used to find fine solutions in many complex hard. However, it has the problem of large amount of computation. In this paper, we propose an automatic extraction method of coronary centerline based on cellular automata. Different from the existing methods, the distance from boundary field is used to extract the centerline of the coronary arteries roughly, which can place the candidate voxel at the center of the coronary arteries and reduce the size of the data. Furthermore, the centerlines of the coronary arteries are extracted accurately by using cellular automata to find the shortest path. The proposed method can extract the centerlines of each branch of the coronary arteries simultaneously from a single source to multiple destinations. Furthermore, compared to the method in Ref [10], our proposed method works well, especially in the case of large vessel curvature, the voxels at the extracted centerline are relatively centered. In the last, quantitative evaluation is conducted on the coronary arteries and the results show that the proposed method achieves a smaller deviation distance.

关键词： Cellular automata Distance from Boundary Coronary arteries Computerized Tomography Angiography

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Review on application progress of federated learning model and security hazard protection

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Digital Communications and Networks 2023年第1期9卷 146-158页

作者： Aimin Yang Zezhong Ma Chunying Zhang Yang Han Zhibin Hu Wei Zhang Xiangdong Huang Yafeng Wu Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina College of Science North China University of Science and TechnologyTangshanHebeiChina College of Artificial Intelligence North China University of Science and TechnologyTangshanHebeiChina

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data *** data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data *** is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our *** the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied *** paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.

关键词： Data silos Machine learning Federated learning Privacy protection Learning framework

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medical entity recognition of Esophageal Carcinoma based on word clustering

Medical entity recognition of Esophageal Carcinoma based on ...

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2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： Wang, Rui Zhao, Jinfeng Peng, Lizhi Yang, Bo Wang, Lin Li, Baosheng University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China Shangdong Medical Imaging and Radiotherapy Engineering Center Jinan250117 China Department of Radiation Oncology Shandong Cancer Hospital Shandong University Jinan250117 China

ISBN: (纸本)9781728105512

Electronic medical Records (EMRs) is the core of medical information system in hospital. EMRs arises from the medical institution, and large amount of clinical data generated every day. Due to the EMRs contains many medical entities and clinical information of the patient, by analyzing and mining the texts data, medical knowledge which closely related to patients or certain diseases can be obtained. In this paper, we ues the EMRs of patients with Esophageal Carcinoma(EC), which include the clinical symptoms of the patients, tests they have undergone, results of the examinations, and the diagnosis and treatment plan. In this paper, word vector training is carried out for large-scale electronic medical record corpus by means of the skip model of word2vec deep learning tool. Then applying the features data to the k-means clustering algorithm to identifying medical entities about EC by word clustering. As the first step of medical knowledge mining, medical entity recognition is the premise of extracting the semantic relationship implied in medical text and structuring EMRs. © 2018 IEEE.

关键词： Semantics

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Application of machine learning on the modelling of diffusion magnetic resonance imaging signal

Application of machine learning on the modelling of diffusio...

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2020 International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2020

作者： Ye, Chen Xu, Daoyun Wang, Lihui Zhu, Yuemin Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang China CNRS INSERM CREATIS UMR 5220 U1206 F-69621 Univ Lyon INSA Lyon Lyon France

The modelling of diffusion Magnetic Resonance Imaging (dMRI) signals is very important for medical clinical application. However, the traditional method is to use a fixed mathematical model to make assumptions about the diffusion-weighted (DW) signals of all regions of human organ, which is unreasonable. In this paper, Convolutional Neural Network (CNN), a machine learning based method is used for learning the different characteristics of the signals, and finally intelligently give multi-model predictions for different regions of human livers. The performance of the proposed method is verified on both simulation and real liver data. The results show that the multi-model predicted by CNN method has high performance in distinguishing normal liver from diseased liver, and has great clinical application prospects © Published under licence by IOP Publishing Ltd.

关键词： Diffusion

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4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1485-1499页

作者： Gang Wang Weisheng Li Mingliang Zhou Haobo Zhu Guang Yang Choon Hwai Yap Chongqing Key Laborotory of Image Rocognition Chongqing University of Posts and TelecommunicationsChongqingChina School of Computing and Data Engineering NingboTech UniversityNingboChina Department of Bioengineering Imperial College LondonLondonUK Key Laboratory of Cyberspace Big Data Intelligent Security(Chongqing University of Posts and Telecommunications) Ministry of EducationChongqingChina School of Computer Science Chongqing UniversityChongqingChina University of Oxford OxfordUK Cardiovascular Research Centre Royal Brompton HospitalLondonUK National Heart and Lung Institute Imperial College LondonLondonUK

Hypoplastic left heart syndrome(HLHS)is a rare,complex,and incredibly foetal congenital heart *** decrease neonatal mortality,evolving HLHS(eHLHS)in pregnant women should be critically diagnosed as soon as ***,diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images,making it difficult to rapidly and easily examine for this ***,the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS(RDeH),which we have named ***,the framework implements a coarseto-fine two-stage detection approach,with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains,and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician *** experiments extensively compare the authors’network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits:(1)it achieved superior average accuracy of 99.37%on three categories of foetal ultrasound images from different cases;(2)it demonstrates visually fine detection performance with weakly supervised *** framework could be used to accelerate the diagnosis of eHLHS,and hence significantly lessen reliance on experienced medical physicians.

关键词： 4D deep learning fetal cardiac ultrasound hypoplastic left heart syndrome weakly-supervised learning

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A novel segmentation method for convex lesions based on dynamic programming with local intra-class variance 12

A novel segmentation method for convex lesions based on dyna...

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27th Annual ACM Symposium on Applied computing, SAC 2012

作者： Yu, Mali Huang, Qiliang Jin, Renchao Song, Enmin Liu, Hong Hung, Chih-Cheng School of Computer Science Huazhong University of Science and Technology Wuhan China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Wuhan Hubei China Radiology Department Second People's Hospital of Nanning Nanning China School of Computing and Software Engineering Southern Polytechnic State University GA United States

ISBN: (纸本)9781450308571

Lesion segmentation plays an important role in medical image processing and analysis. There exist several successful dynamic programming (DP) based segmentation methods for general images. In those methods, the gradient is used as an important factor in the cost function to attract the contours to the boundaries. Since medical images have their characteristics such as low contrast, blurred edges and high noises, the gradient operator cannot work well enough to achieve a satisfactory performance for boundary detection. We define the local intra-class variance and combine it with the dynamic programming method to replace the traditional gradient operation. Experiments on synthetic and X-ray images are carried out and the results are compared with Canny and fast multilevel fuzzy edge detection (FMFED) algorithms. It is demonstrated that the proposed method performs better on medical images with low contrast, blurred edges and high noises. In addition, 483 regions of interest of mammograms randomly extracted from DDSM of the University of South Florida are used to compare our proposed method with the plane-fitting and dynamic programming method (PFDP), and the normalized cut segmentation method (Ncut). The results demonstrate that our method is more accurate and robust than PFDP and Ncut. © 2012 ACM.

关键词： Dynamic programming

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An Accuracy Adaptive Breast Tumor Gene Classification Method

An Accuracy Adaptive Breast Tumor Gene Classification Method

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2013 IEEE International Conference on medical Imaging Physics and Engineering

作者： Yue Zhao Luxuan Qu Hongbing Hu Lei Chen Sino-Dutch Biomedical and Information Engineering School Northeastern University Key Laboratory of Medical Image Computing Ministry of EducationNortheastern University

The method on gene classification has been widely studied with the development of gene *** learning is the best choice to research the *** both traditional SVM and ELM cannot fulfill the requirement of high accuracy and short ***,in this paper,we propose a novel Accuracy Adaptive Extreme Learning Machine(A 2-ELM) which can cover the shortage of traditional SVM and ELM in the fact of more ***,we propose a method of feature selection and overview the property of traditional ***,an Accuracy of Adaptive ELM(A 2-ELM) is developed,which can fulfill the requirement for accurately and ***,we conduct experiments on gene expression data to verify the dynamic and accurate of our proposed accuracy of adaptive ELM in classification gene expression data with experimental settings.

关键词： Breast Tumor,Gene Classification,Accuracy Adaptive,Extreme Learning Machine

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ECG and pulse variability analysis for exercise evaluation

ECG and pulse variability analysis for exercise evaluation

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2011 IEEE International Conference on Automation and Logistics, ICAL 2011

作者： Xu, Lisheng Zhong, Yue Yin, Sainan Zhang, Yuemin Shen, Yanhua Hao, Deguo Hu, Yiming Zhang, Ruifeng Sino-Dutch Biomedical and Information Engineering School Northeastern University Shenyang City Liaoning Province 110004 China Key Laboratory of Medical Image Computing Northeastern University Ministry of Education China Department of Physical Education Northeastern University Shenyang City Liaoning Province 110004 China

ISBN: (纸本)9781457703010

ECG and pulse waveforms are important physiological parameters that can be utilized to analyze noninvasively physical condition and monitor cardiovascular diseases. In recent years, they were studied for evaluating the effects of exercise on human body. However, the systematical analysis on the changes of ECG and pulse waveforms before and after exercise has not been found. This study examines the change of ECG and pulse waveforms after 12-minute-running exercise in nine weeks with 10 healthy subjects. In the experiment, RQ, SQ and TQ represent the amplitude difference between R-, S-, T- and Q-wave of ECG, respectively. Similarly, H1 and H2 represent the amplitude of highest and small peak relative to the baseline of pulse waveforms, respectively. It was found that 90% of subjects' heart rate (HR) at rest has significant decreased (p0.001) during the experiment period. Moreover, other parameters (RQ/SQ/HR, TQ/RQ/HR, baseline-signal ratio and H1/H2) all have significant difference (p0.001) in nine weeks. 80% of RQ/SQ/HR, 60% of TQ/RQ/HR, 100% of baseline-signal energy ratio and 80% of H1/H2 at rest decreased during nine weeks experiment;while 70% of RQ/SQ/HR declined, 100% of TQ/RQ/HR and 90% of H1/H2 increased immediately after exercise comparing with rest condition. Results showed that these parameters could properly denote the change of ECG and pulse waveforms in exercise experiment. Furthermore, according to the variation trend of these parameters, it indicated that 12-minute-running improved cardiopulmonary function of subjects significantly. © 2011 IEEE.

关键词： Electrocardiograms

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How does four-dimensional CT spare normal tissues in non-small cell lung cancer radiotherapy by defining internal target volume?

How does four-dimensional CT spare normal tissues in non-sma...

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第八届中国肿瘤学术大会暨第十三届海峡两岸肿瘤学术会议

作者： Tong Bai Jian Zhu Yong Yin Jie Lu Huazhong Shu Lin Wang Bo Yang Department of Radiation Physics Shandong Cancer Hospital and Institute Laboratory of Image Science and Technology Southeast University Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan

Purpose To investigate how does the four-dimensional CT(4DCT)technique spare normal tissues in non-small cell lung cancer(NSCLC)radiotherapy by defining individualized internal target volume(ITV).Methods GTV and CTV w... 详细信息

关键词： NSCLC PTV How does four-dimensional CT spare normal tissues in non-small cell lung cancer radiotherapy by defining internal target volume

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