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检索条件"机构=Key Laboratory of Intelligent Computing in Medical Image MIIC"

共 241 条记录，以下是81-90 订阅

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An adaptive clustering algorithm of gaussian curve fitting the histogram based on K-means

An adaptive clustering algorithm of gaussian curve fitting t...

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2019 IEEE International Conferences on Ubiquitous computing and Communications and Data Science and Computational Intelligence and Smart computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

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

ISBN: (纸本)9781728152097

The K-mean algorithm is simple and efficient in vascular segmentation. But in a K-means clustering algorithm, the number of clustering centers must be determined in advance and the random allocation of clustering centers seriously affects the efficiency of the algorithm. In order to solve this problem, an adaptive clustering algorithm based on K-means with the Gaussian curve fitting histogram is proposed, which is used to segment circular-like blood vessels. Firstly, the histogram outline is fitted into the Gaussian curve by the least square method and the maximum value of the curve is obtained. The number of maximal values is counted as the number of clustering centers and the maximum points are used as the central points of clustering. Also, the K-means algorithm is improved by combining the intensity and position information of pixels. The proposed method has been tested by 6 groups of medical images. The algorithm has the advantages of less iterations, fast convergence speed and getting more than 95% Dice Similarity Measure value. The algorithm can effectively solve the determination of multi-seed points and vascular segmentation in medical image segmentation. © 2019 IEEE.

关键词： K-means clustering

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Build emotion lexicon from microblogs by combining effects of seed words and emoticons in a heterogeneous graph 15

Build emotion lexicon from microblogs by combining effects o...

引用

26th ACM Conference on Hypertext and Social Media, HT 2015

作者： Song, Kaisong Feng, Shi Gao, Wei Wang, Daling Chen, Ling Zhang, Chengqi School of Information Science and Engineering Northeastern University Shenyang China Key Laboratory of Medical Image Computing Northeastern University Ministry of Education China Qatar Computing Research Institute Hamad Bin Khalifa University Doha Qatar Centre for Quantum Computation and Intelligent Systems University of Technology Sydney Australia

ISBN: (纸本)9781450333955

As an indispensable resource for emotion analysis, emotion lexicons have attracted increasing attention in recent years. Most existing methods focus on capturing the single emotional effect of words rather than the emotion distributions which are helpful to model multiple complex emotions in a subjective text. Meanwhile, automatic lexicon building methods are overly dependent on seed words but neglect the effect of emoticons which are natural graphical labels of fine-grained emotion. In this paper, we propose a novel emotion lexicon building framework that leverages both seed words and emoticons simultaneously to capture emotion distributions of candidate words more accurately. Our method overcomes the weakness of existing methods by combining the effects of both seed words and emoticons in a unified three-layer heterogeneous graph, in which a multi-label random walk (MLRW) algorithm is performed to strengthen the emotion distribution estimation. Experimental results on real-world data reveal that our constructed emotion lexicon achieves promising results for emotion classification compared to the state-of-the-art lexicons. © 2015 ACM.

关键词： Computer programming

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An Adaptive Speech Noise Reduction Method Based on Noise Classification

An Adaptive Speech Noise Reduction Method Based on Noise Cla...

引用

IEEE International Conference on Cyber Technology in Automation, Control, and intelligent Systems

作者： Ben Niu Yangjie Wei Key Laboratory of Intelligent Computing in Medical Image Ministry of Education College of Computer Science and Engineering Northeastern University Shenyang

To improve the accuracy and robustness of existing speech denoising methods, this study proposes an adaptive speech noise reduction method based on noise classification. First, to adapt to different time-varying characteristics of various noises in complex environments, a new acoustic feature matrix is constructed by combining the LogFbank features and the perceptual linear prediction coefficients, and a noise classifier based on the support vector machine is designed to classify the experimental noise. Then, to address the problem of “music noise” in traditional speech denoising methods, the voice activity detection threshold is adaptively updated according to the judgment result of background noise types, and the optimal parameters for the improved minima controlled recursive averaging noise estimation algorithm are obtained to accurately estimate current noise and reduce it. Finally, the performance of the proposed method is evaluated using a serial of experiments on different situations, and the experimental results prove the effectiveness and feasibility of our proposed method in this study.

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BECNN: Bias Field Estimation CNN Trained with a Dual Route Implicit Supervised Learning Strategy

BECNN: Bias Field Estimation CNN Trained with a Dual Route I...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Shuaizheng Feng, Chaolu Li, Wei Yang, Jinzhu Zhao, Dazhe Northeastern University School of Computer Science And Engineering Shenyang China Ministry of Education Northeastern University Key Laboratory of Intelligent Computing in Medical Image Shenyang China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China

ISBN: (纸本)9798350386226

Bias fields adversely affect various automatic analysis technologies. Therefore, bias field correction is essential. However, deep learning based methods encounter challenges in obtaining ground truth. Although existing methods attempt to address this problem by using training-free techniques or constructing datasets with approximations of the ground truth, the lack of task-oriented guidance, training instability, and inappropriate use of approximations still impact performance. Additionally, different approaches for bias field correction, i.e., estimating bias fields versus directly restoring clean images, exhibit different performances. However, there is no consensus on the best way, resulting in limited performance in some cases. To address these problems, we propose a bias field generation method to construct the dataset and provide task-oriented information. We then propose the concept of Equivalent of Residual Mapping (ERM) to analyze the advantages of estimating bias fields. According to ERM, we propose the Bias field Estimation Convolutional Neural Network (BECNN). Finally, we propose the Dual Route Implicit Supervised Learning (DRISL) strategy to balance the guidance derived from approximations of the ground truth with over-dependence on them. The proposed method is compared qualitatively and quantitatively with the correlated methods. Experiment results demonstrate that the proposed method performs effectively both on bias field estimation and correction. © 2024 IEEE.

关键词： Self-supervised learning

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A Lightweight Federated Learning Framework in a One-way Active Communication Environment

A Lightweight Federated Learning Framework in a One-way Acti...

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Frontiers of Computer Science and Information Technology (FCSIT), Euro-Asia Conference on

作者： Wei Li Xiang Cheng Key Laboratory of Intelligent Computing in Medical Image Shenyang China Computer Science and Engineering Northeastern University Shenyang China

In the medical field, patient data is protected by privacy and cannot be made public. Meanwhile, the medical institution is in the local area network, and the outside world cannot directly access it. In this case, data is not available, nor can a centralized machine learning approach be used to benefit from the data. Federated learning is a distributed machine learning method that solves the problem that data cannot be centralized for training like traditional machine learning because data is scattered and limited in access. This paper proposes a federated learning framework that enables decentralized machine learning. In the framework, we use a one-way active communication method that is not adopted by the current mainstream federated learning frameworks. The framework only allows clients to actively access the server, and multiple clients access the server asynchronously. The design of this communication method ensures the data security of the client in the framework. The framework proposed in this paper is flexible and lightweight. It does not depend on complex configuration and environment, and has strong scalability. Finally, the proposed framework is validated on federated learning tasks, proving the usability and effectiveness of the framework.

关键词： Training Federated learning Hospitals Scalability Distributed databases Servers Usability

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Combining HWEBING and HOG-MLBP features for pedestrian detection

Combining HWEBING and HOG-MLBP features for pedestrian detec...

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作者： Xiao, Ling Zhang, Yongjun Zhang, Jian Wang, Qian Li, Yuewei Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Guiyang China School of Computer Science and Technology Guizhou University Guiyang China School of Computing and Communications University of Technology Sydney Sydney Australia

Pedestrian detection has vital value in many areas such as driver assistance systems, driverless cars, intelligent tourism systems etc., but there are some difficulties that need to be solved. The algorithm with high detection rate is complex and requires substantial time. Therefore, how to improve the detection accuracy and speed has become the key of pedestrian detection. For these reasons, firstly, an improved algorithm, called hash and window enhancement of binarised normed gradients (HWEBING), based on binarised normed gradients feature is proposed. Subsequently, the authors present an improved local texture feature, namely mean of local binary pattern (MLBP), based on uniform pattern local binary pattern (ULBP) for increasing the detection rate. Finally, after using the HWEBING algorithm to get the candidate windows, the combination of MLBP feature and histograms of oriented gradients feature is extracted from these windows to further enhance the detection accuracy. Experimental results reveal that speed of using the HWEBING algorithm for pre-detection is 5.5 times faster than the traditional method of pedestrian detection. Furthermore, the detection rate of MLBP feature is 3.5 and 2.1% higher than those of ULBP and basic pattern local binary pattern (Basic-LBP), respectively. © 2018 Institution of Engineering and Technology. All rights reserved.

关键词： Local binary pattern

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Temporal Graph Representation Learning for Autism spectrum disorder Brain Networks

Temporal Graph Representation Learning for Autism spectrum d...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Cao, Peng Wen, Guangqi Li, Lanting Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar Northeastern University College of Computer Science and Engineering Key Laboratory of Intelligent Computing in Medical Image Shenyang China Northeastern University College of Computer Science and Engineering Shenyang China A.I. Lab Alibaba Hangzhou China Amii University of Alberta Edmonton Canada

ISBN: (纸本)9781665401265

Modeling spatio-temporal dynamics in functional brain networks is critical for underlying the functional mechanism of autism spectrum disorder (ASD). In our study, we propose an end-to-end framework called temporal graph representation learning for brain networks, which thoroughly captures spatio-temporal features in resting-state functional magnetic resonance imaging (rs-fMRI) data. Specifically, we first transform rs-fMRI time-series into temporal multi-graph using a sliding window technique. A temporal multi-graph clustering is then designed to eliminate the inconsistency of the temporal multi-graph series. Then, a graph structure aware LSTM (GSA-LSTM) is proposed to capture the spatio-temporal embedding for temporal graphs. The proposed GSA-LSTM can not only capture discriminative features for prediction but also impute the incomplete graphs for the temporal multi-graph series. Extensive experiments on autism brain imaging data exchange (ABIDE) dataset shows the effectiveness of our proposed framework. The results demonstrate that the proposed dynamic brain network embedding learning outperforms the state of-the-art brain network classification models. Furthermore, the obtained clustering results are consistent with the previous neuroimaging-derived evidence of biomarkers for autism spectrum disorder (ASD). © 2021 IEEE.

关键词： Embeddings

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Cross-Domain Retinopathy Classification with OCT images via Disentangling Representation and Adaptation Networks

Cross-Domain Retinopathy Classification with OCT Images via ...

引用

Optoelectronics Global Conference (OGC)

作者： Wickramaarachchi Minidu Thiranjaya Yuan Li Lei Tao Yuemei Luo Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC) School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC) School of Computer Science Nanjing University of Information Science and Technology Nanjing China Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC) School of Future Technology Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350391466

ISBN: (纸本)9798350391473

Deep learning methods have shown significant potential in retinopathy classification using optical coherence tomography (OCT) images. However, substantial challenges arise due to domain shift issues, which are attributable to heterogeneity in data collection devices, variations in subject populations, and discrepancies in imaging protocols and parameters. To address these, we propose a novel approach leveraging Disentangling Representation and Adaptation Networks (DRANet) to enhance the robustness and generalization of retinopathy classification models across different OCT datasets. Our methodology involves developing DRANet, a specialized network architecture designed to disentangle representations of data and facilitate domain adaptation. DRANet includes components for learning invariant representations across domains through adversarial training, ensuring robustness against domain shifts. DRANet encodes individual representations of content and style from both source and target images. Then, it adapts the domain by incorporating the transferred style factor into the content factor, along with learnable weights specified for each domain. This learning framework allows bidirectional domain. After conducting extensive experiments conducted on three public datasets achieved accuracies of $\mathrm{8 8 \%, ~} \mathrm{9 0 \%}$, and $\mathrm{9 2 \%}$ under three cross-domain scenarios, demonstrating the efficacy of our approach in mitigating domain shift effects and improving retinopathy classification performance.

关键词： Training Deep learning Protocols Retinopathy Optical coherence tomography Imaging Coherence Network architecture Data collection Robustness

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What will regularized continuous learning performs if it was used to medical image segmentation: a preliminary analysis

What will regularized continuous learning performs if it was...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Weihao Dai Chaolu Feng Shuaizheng Chen Wei Li Jinzhu Yang Dazhe Zhao College of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image (MIIC) Ministry of Education Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China

Regularization-based continuous learning (RCL) has been proven to be highly effective on struggling against catastrophic forgetting. However, its application in medical image segmentation (MIS) is relatively scarce. In this paper, we provide a unified description of 6 RCL methods (LwF, LwM, EWC, SI, MAS, and PIGWM) using Taylor expansion and investigate their performances in 2 classic MIS scenarios, namely retinal vessel segmentation (RVS) and cardiac left ventricle segmentation (CLVS) on 8 datasets (CHASE, DRHAGIS, RITE and STARE for the former and M&Ms, LVSC, ACDC and SCD for the latter). We also explore the influence of different task orders (easy to hard or hard to easy), optimizers (Adam or SGD), and parameter capacities (2, 3, 4 or 5 down- and up-sampling pairs) on the performance of these methods. Our experimental results show that these methods are capable of mitigating catastrophic forgetting to a certain extent. Comparing to a hard-to-easy order, most of the methods perform better on all of the already known tasks in an easy-to-hard order. Optimizer Adam performs better on RVS and CLVS. Capacity increases are obviously effective for CLVS, but they have no significant impact on RVS.

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Segmentation of Lung Parenchyma Based on FPN++Mask R-CNN Model 2020

Segmentation of Lung Parenchyma Based on FPN++Mask R-CNN Mod...

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The Fourth International Symposium on image computing and Digital Medicine

作者： Xingfei Xu Gen Li Xiaoshuo Li Pan Liu Wenjun Tan College of Computer Science and Engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education China

ISBN: (纸本)9781450389686

In recent years, the number of lung cancer patients has continued to increase. In the process of detecting lung cancer, accurate segmentation of lung parenchyma plays a key role. In this paper, we proposed a method of lung parenchyma segmentation based on FPN++Mask R-CNN neural network model. The model improved original Mask R-CNN networks and optimized the structure of FPN (Feature Pyramid Networks), which is the feature extraction model of Mask R-CNN, by expanding the scale and level of FPN to fuse and extract more picture feature information from different levels. The experimental results show that compared with original Mask R-CNN models, FPN++Mask R-CNN demonstrates better segmentation results.

关键词： Lung parenchyma segmentation Feature Pyramid Networks Mask R-CNN

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