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中国生物医学工程学报最新文献

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Classification of breast mass in 3D ultrasound images with annotations based on convolutional neural networks 基于卷积神经网络注释的三维超声图像乳腺肿块分类
Q4 Medicine Pub Date : 2018-08-20 DOI: 10.3969/J.ISSN.0258-8021.2018.04.004
Xiaohan Kong, T. Tan, L. Bao, Guangzhi Wang
The automatic classification of breast tumor in ultrasound images is of great significance to improve doctors' efficiency and reduce the rate of misdiagnosis. The novel 3D breast ultrasound data contains more information for diagnosis, but images from different directions have their distinct performance as a result of this ultrasound imaging mechanism. For this breast ultrasound data, this paper designed three kinds of convolutional neural network model using its flexibility and characteristic of learning automatically, and the three models were able to accept transverse plane images, transverse plane and coronal plane images, images and annotations information. The effects of different information fusion on the accuracy of breast tumor classification were investigated. A dataset contains 880 images (i. e., 401 benign images, 479 malign images) and their annotations were employed, and we performed 5-fold cross validation to calculate the accuracy and AUC of each model. The experimental results indicated that the models designed in this paper can deal with the images and annotations simultaneously. Compared with the single-input model, the multi-information fusion model improved the accuracy of classification by 2.91%, and achieved the accuracy of 75.11% and AUC of 0.8294. The proposed models provided a reference for the classification application of convolutional neural networks with multi-information fusion.
乳腺肿瘤超声图像自动分类对提高医生工作效率、降低误诊率具有重要意义。新的乳腺三维超声数据包含了更多的诊断信息,但由于这种超声成像机制,不同方向的图像具有不同的性能。针对该乳腺超声数据,本文利用其灵活性和自动学习的特点,设计了三种卷积神经网络模型,三种模型均能接受横切面图像、横切面和冠状面图像、图像和注释信息。研究了不同信息融合对乳腺肿瘤分类准确率的影响。数据集包含880张图像(即401张良性图像,479张恶性图像),并使用它们的注释,我们进行了5倍交叉验证,以计算每个模型的准确率和AUC。实验结果表明,本文设计的模型可以同时处理图像和注释。与单输入模型相比,多信息融合模型的分类准确率提高了2.91%,准确率达到75.11%,AUC为0.8294。该模型为多信息融合卷积神经网络的分类应用提供了参考。
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引用次数: 5
Multichannel EEG-EMG coupling analysis using a variable scale symbolic transfer entropy approach 使用可变尺度符号传递熵方法的多通道EEG-EMG耦合分析
Q4 Medicine Pub Date : 2018-02-01 DOI: 10.3969/J.ISSN.0258-8021.2018.01.002
Gao Yunyuan, Leilei Ren, Xu Zhou, Qizhong Zhang, Yingchun Zhang
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引用次数: 1
Applying visual perception information for detection analysis and automatic extraction of breast mass in mammograms 应用视觉感知信息进行乳房x光片肿块的检测分析与自动提取
Q4 Medicine Pub Date : 2014-02-20 DOI: 10.3969/J.ISSN.0258-8021.2014.01.005
E. Ke, Li Hua Li, Wei Liu, W. Xu, Juan Zhang, Ling Zhang, B. Zheng
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引用次数: 0
Research on Medical Image Fusion Algorithms Based on Nonsubsampled Contourlet 基于非下采样Contourlet的医学图像融合算法研究
Q4 Medicine Pub Date : 2014-01-01 DOI: 10.1007/978-81-322-1695-7_64
Junyu Long, Hong Yu, Aiming Yu
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引用次数: 1
Common spatial spectral pattern for motor imagery tasks in small channel configuration 小通道配置中运动成像任务的常见空间光谱模式
Q4 Medicine Pub Date : 2013-10-20 DOI: 10.3969/J.ISSN.0258-8021.2013.05.07
Jianjun Meng, X. Sheng, Lin Yao, Xiangyang Zhu
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引用次数: 2
Carbon Nanotube as Ultrasensitive Biosensor for DNA Detection: A First-Principles Calculation 碳纳米管作为DNA检测的超灵敏生物传感器:第一性原理计算
Q4 Medicine Pub Date : 2013-01-01 DOI: 10.1007/978-3-642-29305-4_85
Yanhong Zhou, Y. Peng, Ling-zhi Yuan
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引用次数: 1
3D anterior chamber angle measurements with high resolution Fourier-domain optical coherence tomography 三维前房角测量与高分辨率傅里叶域光学相干断层扫描
Q4 Medicine Pub Date : 2012-12-20 DOI: 10.3969/J.ISSN.0258-8021.2012.06.005
Wei Wu, H. Duan, Yan Li, C. Jiang, Bing Qin, David Huang
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引用次数: 0
Advection-diffusion-reaction equations based tumor cells growth modeling 基于平流-扩散-反应方程的肿瘤细胞生长模型
Q4 Medicine Pub Date : 2012-10-20 DOI: 10.3969/J.ISSN.0258-8021.2012.05.006
L. Zhang, M. Jiang, An De Bao
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引用次数: 1
A Classification Method for RNA Splicing Regulatory Elements RNA剪接调控元件的分类方法
Q4 Medicine Pub Date : 2012-02-20 DOI: 10.3969/J.ISSN.0258-8021.2012.01.008
Meng Ma, Yang Wang, Ying Ru, Zefeng Wang
The sequence classification methods have broad application in various bioinformatics areas such as the identification of regulatory elements of transcription and the prediction of protein structure.Here we presented a new classification method to analyze short sequences based on their sequential features,and used this method to study RNA splicing regulatory elements.This method extracted the sequential features from the known spicing regulatory elements,and developed a scoring system to evaluate how possible a given short sequence can regulate RNA splicing.This method was compared with some other methods through applying to a set of exonic splicing enhancer(ESE) and silencer(ESS) octamers.The average prediction accuracy of this sequential feature-based method for three kinds of computation validation experiments reached about 93% and the transparent predictive structure of the method helps to interpret the biological mechanism.This paper shows a new method for biology series' data analysis and presents a new way for the study of regulatory sequences that control gene expression.
序列分类方法在转录调控元件的鉴定、蛋白质结构的预测等生物信息学领域有着广泛的应用。本文提出了一种基于序列特征分析短序列的新分类方法,并利用该方法对RNA剪接调控元件进行了研究。该方法从已知的香料调节元件中提取序列特征,并开发了一个评分系统来评估给定短序列对RNA剪接的调节可能性。将该方法应用于一组外显子剪接增强子(ESE)和沉默子(ESS)八聚体,并与其他方法进行了比较。基于序列特征的方法在三种计算验证实验中的平均预测准确率达到93%左右,该方法透明的预测结构有助于解释生物机制。本文提出了一种生物序列数据分析的新方法,为研究控制基因表达的调控序列提供了一条新途径。
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引用次数: 0
Analyzing EEG of Quasi-brain-death Based on Approximate Entropy Measures 基于近似熵测度的准脑死亡脑电分析
Q4 Medicine Pub Date : 2011-01-01 DOI: 10.1007/978-90-481-9695-1_110
Kun Yang, Qi-Wei Shi, Jianting Cao, Jianting Cao, Jianting Cao, Rubin Wang, Hui-Li Zhu, Zhaoshui He
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引用次数: 3
期刊
中国生物医学工程学报
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