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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Yu, Yao Li, Qirui Zhang, Qieshi Hu, Wenjian Liu, Shumei Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China

ISBN: (纸本)9781728162676

Blockchain has unique advantages in data privacy protection and data integrity. We can solve many security problems, such as the single point of failure and data sharing in the current centralized system through blockchain approach. Existing studies have demonstrated that the application of blockchain in the medical system could improve the patient's medical experience. However, we found that the blockchain-based medical systems didn't consider the problems of long insurance claim cycle and complicated procedures. There are also very few healthcare systems that provide targeted sharing protocols for medical data and personal health data. In this paper, we propose a multi-role healthcare data sharing system framework based on blockchain. In this system, we reduce the storage cost through the collaborative storage of blockchain and IPFS. Then we design a smart contract on insurance to help patients achieve automatic insurance claim. In addition, we design two different sharing protocols to realize the fine management of personal data. The system analysis shows that our proposed blockchain-based multi-role healthcare data sharing system can effectively address the actual needs of users and has perfect performance in data storage, privacy protection, insurance claims and personal data management. © 2021 IEEE.

关键词： Blockchain

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Spiking neural P systems for arithmetic operations

Spiking neural P systems for arithmetic operations

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6th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2011

作者： Zeng, Xiangxiang Song, Tao Pan, Linqiang Zhang, Xingyi Key Laboratory on Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Lab of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9780769545141

Recently, Gutiérrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bioinspired computing devices - spiking neural P systems (for short, SN P systems). However, the binary encoding mechanism used in their research looks like the encoding approach in electronic circuits, instead of the style of spiking neurons (in usual SN P systems, information are encoded as the time interval between spikes). In this work, three SN P systems are constructed as adder, subtracter and multiplier, respectively. In these devices, a number is inputted to the system as the interval of time elapsed between two spikes received by input neuron, the result of a computation is the time between the moments when the output neuron spikes. © 2011 IEEE.

关键词： Encoding (symbols)

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A content-based image retrieval scheme for identifying lung nodule malignancy levels 29

A content-based image retrieval scheme for identifying lung ...

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第29届中国控制与决策会议

作者： Guohui Wei He Ma Wei Qian Xinzhuo Zhao Sino-Dutch Biomedical and Information Engineering School Hunnan CampusNortheastern University Key Laboratory of Medical Image Computing Ministry of Education College of Engineering University of Texas at El Paso

ISBN: (纸本)9781509046584

Discriminating the lung nodules benign or malignant is an important task for computer aided diagnosis of lung *** malignancy of the nodules is divided into five levels in Lung image Database Consortium(LIDC) *** this study,a new content-based image retrieval(CBIR) scheme is proposed for classification of the lung nodules with different ratings.A lung nodule dataset is assembled from LIDC lung CT *** nodule density dependent features are calculated to depict each *** each queried nodule,a CBIR scheme is used to search for ten most similar reference nodules.A malignancy probability is computed to predict the malignancy of the lung *** results show that accuracy and the area under the curve(AUC) are 0.6655 and 0.6901 when classifying nodules moderately suspicious for cancer(rating 4) and highly suspicious(rating 5).The ACC and AUC are 0.9231 and 0.8659 when testing our scheme on differentiating benign and malignant cases.

关键词： content-based image retrieval computer-aided diagnosis lung nodules classification heterogeneity lesion density

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Texture Features and Machine Learning Based Environmental Microorganism Microscopic image Classification

Texture Features and Machine Learning Based Environmental Mi...

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International Conference on image, Vision and intelligent Systems, ICIVIS 2023

作者： Ouyang, Xinyu Yuan, Huaqian Zhou, Shangqi Fu, Yanyu Yang, Jinzhu Grzegorzek, Marcin Wang, Yuexi Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Department of Rehabilitation Shengjing Hospital of China Medical University Shenyang China

ISBN: (纸本)9789819708543

In recent years, with the continuous development of molecular biology technology, the effective use of environmental microorganisms to solve the problem of pollution in the ecological environment has been widely used. However, the original microbial cultivation, environmental application and monitoring have the disadvantages of high degree of specialization, long time and high cost. In this case, the use of computer image processing and machine learning techniques for microbial identification and classification becomes a more efficient, economical and necessary method. This paper presents an environmental microorganism microscopic image classification method based on texture features and machine learning. In the research and experiments, microscopic images of microorganisms are cropped, grayed, data augmented, target areas segmented and extracted four types of texture features. The training set and test set are divided in a 1:1 ratio, and different recognition results are obtained based on different texture feature vectors and classifier models. Ultimately, the gradient-gray co-occurrence matrix feature combined with the Random Forest algorithm classifier achieves an F1-measure value of nearly 40%, which improves the work efficiency of environmental microbial researchers and related field workers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Microorganisms

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Geometric constraint solving based on cell membrane optimization

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Information Technology Journal 2012年第4期11卷 496-499页

作者： Cao, Chunhong Chuan, Tang Zhao, Dazhe Zhang, Bin College of Information Science and Engineering Northeastern University Shenyang 110819 China State Key Laboratory of Geohazard Prevention and Geo Environment Protection Chengdu University of Technology Chengdu 610059 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang Lining 110819 China State Key Laboratory for Novel Software Technology Nanjing University China

Geometric constraint problem is equivalent to the problem of solving a set of nonlinear equations substantially. The constraint problem can be transformed to an optimization n problem. We can solve the problem with cell membrane optimization. By studying the characteristics of cell membrane and the mode of material transfer, proposed a new global optimization algorithm: Cell Membrane Optimization (CMO), combined with global optimization algorithm. The experiment shows that it can improve the geometric constraint solving efficiency and possess better convergence property than the compared algorithms. © 2012 Asian Network for Scientific Information.

关键词： Global optimization

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Learning discriminative foreground-and-background features for few-shot segmentation

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Multimedia Tools and Applications 2024年第18期83卷 55999-56019页

作者： Jiang, Cong Zhou, Yange Liu, Zhaoshuo Feng, Chaolu Li, Wei Yang, Jinzhu School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China College of Science Northeastern University Liaoning Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang110819 China

Few-shot Semantic Segmentation (FSS) endeavors to segment novel categories in a query image by referring to a support set comprising only a few annotated examples. Presently, many existing FSS methodologies primarily embrace the prototype learning paradigm and concentrate on optimizing the matching mechanism. However, these approaches tend to overlook the discrimination between the features of foreground background. Consequently, the segmentation results are often imprecise when it comes to capturing intricate structures, such as boundaries and small objects. In this study, we introduce the Discriminative Foreground-and-Background feature learning Network (DFBNet) to enhance the distinguishability of bilateral features. DFBNet comprises three major modules: a multi-level self-matching module (MSM), a feature separation module (FSM), and a semantic alignment module (SAM). The MSM generates prior masks separately for the foreground and background, employing a self-matching strategy across different feature levels. These prior masks are subsequently used as scaling factors within the FSM, where the features of the query’s foreground and background are independently scaled up and then concatenated along the channel dimension. Furthermore, we incorporate a two-layer Transformer encoder-based semantic alignment module (SAM) in DFBNet to refine the features, thereby creating a greater distinction between the foreground and background features. The performance of DFBNet is evaluated on the PASCAL-5i and COCO-20i benchmarks, demonstrating its superiority over existing solutions and establishing new state-of-the-art results in the field of few-shot semantic segmentation. The codes will be released if this paper is accepted. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Semantic Segmentation

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Sparse multi-kernel based multi-task learning for joint prediction of clinical scores and biomarker identification in Alzheimer’s disease 20th

Sparse multi-kernel based multi-task learning for joint pred...

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20th International Conference on medical image computing and Computer-Assisted Intervention, MICCAI 2017

作者： Cao, Peng Liu, Xiaoli Yang, Jinzhu Zhao, Dazhe Zaiane, Osmar Key Laboratory of Medical Image Computing of Ministry of Education College of Computer Science and Engineering Northeastern University Shenyang Shi China University of Alberta Edmonton Canada

ISBN: (纸本)9783319661780

Machine learning methods have been used to predict the clinical scores and identify the image biomarkers from individual MRI scans. Recently, the multi-task learning (MTL) with sparsity-inducing norm have been widely studied to investigate the prediction power of neuroimaging measures by incorporating inherent correlations among multiple clinical cognitive measures. However, most of the existing MTL algorithms are formulated linear sparse models, in which the response (e.g., cognitive score) is a linear function of predictors (e.g., neuroimaging measures). To exploit the nonlinear relationship between the neuroimaging measures and cognitive measures, we consider that tasks to be learned share a common subset of features in the kernel space as well as the kernel functions. Specifically, we propose a multi-kernel based multi-task learning with a mixed sparsity-inducing norm to better capture the complex relationship between the cognitive scores and the neuroimaging measures. The formation can be efficiently solved by mirror-descent optimization. Experiments on the Alzheimers Disease Neuroimaging Initiative (ADNI) database showed that the proposed algorithm achieved better prediction performance than state-of-the-art linear based methods both on single MRI and multiple modalities. © Springer International Publishing AG 2017.

关键词： Forecasting

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Skin Cancer image Identification Using Deep Convolutional Neural Networks

Skin Cancer Image Identification Using Deep Convolutional Ne...

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International Conference on image, Vision and intelligent Systems, ICIVIS 2023

作者： Zhang, Wenbo Sun, Xiaolin Ma, Shiyi Yang, Jinzhu Grzegorzek, Marcin Wang, Yuexi Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Department of Rehabilitation Shengjing Hospital of China Medical University Shenyang China

ISBN: (纸本)9789819708543

Skin cancer is a type of cancer and has emerged as one of the most prevalent types over the past decade. Early diagnosis and treatment are a key factor in the treatment of skin cancer. The traditional clinical diagnosis method of skin cancer is dermoscopy. On account of the complexity of the skin image, occasional instances of missed diagnosis and misdiagnosis arise, leading to delayed optimal treatment for patients. Two different networks, Inception-V3 and ResNet-50, are put forward in this paper. The dataset is separated into two categories, and the network performance is evaluated using evaluation indicators. Findings indicate that the ResNet50 network attains a higher accuracy rate of 88.83% compared to the Inception-V3 network's 83.17% accuracy. Additionally, the other three indicators of ResNet-50 are also surpass those of Inception-V3. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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Dual Attention Poser: Dual Path Body Tracking Based on Attention

Dual Attention Poser: Dual Path Body Tracking Based on Atten...

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

作者： Di, Xinhan Dai, Xiaokun Zhang, Xinkang Chen, Xinrong Fudan Universiry Academy for Engineering&Technology China Deepearthgo Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China

ISBN: (纸本)9798350302493

Currently, mixed reality head-mounted displays tracking the full body of users is an important human-computer interaction mode through the pose of the head and the hands. Unfortunately, users' virtual representation and experience is limited due to high reconstruction error when simple transformer network architecture is applied. In this paper, we present a novel model, named Dual Attention Poser, which can learn the whole body reconstruction at a high accuracy. The proposed model consists of three key modules. Among them, dual-path attention encoder is designed to extract feature of the sparse signals. Cross attention mixer module enable the fusion of representation in the double path. Attention-gated-mlp decoder is applied to decode the hidden feature from the sparse input through attention gate. Test results on the AMASS dataset show that Dual Attention Poser can reduce the error by up to 18.2% in comparison with the state-of-the-art results. © 2023 IEEE.

关键词： Network architecture

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An accelerative method for multimodality medical image registration based on CUDA

An accelerative method for multimodality medical image regis...

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4th International Congress on image and Signal Processing, CISP 2011

作者： Yang, Jinzhu Han, Fangfang Fen, Chaolu Zhao, Dazhe Wang, Yanfei Key Laboratory of Medical Image Computing of Ministry of Education Northeast University Shenyang China MR Division Liaoning Kampo Medical System Co. Ltd. Shenyang China

ISBN: (纸本)9781424493067

image registration algorithm based on normal mutual information (NMI) has been adopted in multimodality medical image registration research for a few years. Basing on the whole information of the image pixels, this method has no incertitude of the problems about the similar parts, different range of values, segmentation, boundary, and so on. The results have always been more satisfied by doctors than other registration methods. However, medical images usually contain a huge amount of data and the calculation of mutual information takes a lot of statistical analyses. Therefore the operation time is too long to apply in clinical. According to this problem, researchers have intended to improve the algorithm on two aspects, namely software and hardware. Compute Unified Device Architecture (CUDA) is a new technology of parallel computing with broad-ranging application on image processing, reconstruction, analysis, and much more. Above all, we proposed a new improvement CUDA based method to accelerate NMI registration algorithm. Experiments show it cannot only ensure to provide a correct registration images but also an efficiency improvement as well. And the total speedup ratio nearly reaches 10. © 2011 IEEE.

关键词： image registration

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