基于支持向量机的肝纤维化CT图像分类

基于统计学习理论中结构风险最小化原则的支持向量机是易于小样本的机器学习方法.本文使用支持向量机和二叉树的方法对肝纤维化CT图像进行分类,并与k近邻法和BP神经网络等其它算法进行比较,结果显示对于肝纤维化图像,支持向量机的分类效果和鲁棒性要高于其他两种算法.     

基于共同空间模式和支持向量机的脑–机接口信号分类

在脑-机接口(BCI)研究中一个关键问题是准确地对EEG信号进行特征提取和模式分类,以得到人机通信与控制命令。经过对非靶刺激和靶刺激下诱发的EEG进行去均值、低通滤波、下采样等处理后,利用共同空间模式算法对所采集到的EEG数据进行特征提取,然后通过网格搜索法获取最优分类参数的情况下,利用基于径向基函数的支持向量机设计分类器。通过对3名受试者的实验数据进行各10次的处理后得到较好的分类效果,平均分类准确率为99.2%。实验结果表明,本文的方法适合于基于"模拟阅读"的脑-机接口中。     

基于多维特征和支持向量机核函数优化的自动化肺结节检测模型

为了解决特征级肺结节检测研究中的特征结构不合理和分类器性能低下两个问题,提出了一种多维特征表达与支持向量机(support vector machine,SVM)核函数优化相结合的自动化肺结节检测模型。首先提取多维特征数据量化感兴趣区域(region of interest,ROI),然后利用网格寻优算法优化SVM核函数,最后基于优化的SVM分类器识别结节区域和非结节区域。仿真实验结果表明,该模型耗时短、检测正确率高,具有一定的临床应用价值。     

基于微钙化检测的计算机辅助诊断系统对于乳腺导管原位癌的诊断价值

目的 探讨计算机辅助诊断系统(CADx)在微钙化检测与特征提取基础上的分类对于导管原位癌(DCIS)的诊断价值.方法 回顾性分析经南方医科大学附属南海医院及中山大学肿瘤防治中心行乳腺X线摄影检查发现微钙化并经病理学证实的623例患者影像资料,其中,良性病变378例,DCIS 245例.用受试者操作特征曲线(ROC)分别分析采用计算机方法提取的每个微钙化特征对于这两类病变判别的诊断效能,和应用所有微钙化特征集合并基于支持向量机(SVM)分类器的CADx的分类诊断效能.结果 CADx对于良性病变和DCIS这两类病变微钙化分类的ROC曲线下面积(Az)为0.853;特异度、准确率、敏感度分别为70.1％、82.1％、90.7％,高于单个微钙化特征的诊断效能.结论 采用CADx对于DCIS微钙化能较好的检测与定位,对乳腺癌早期病变的识别能提供有益的参考.     

EMD和SVM结合的脑电信号分类方法

脑电(EEG)癫痫波的自动检测与分类在临床医学上具有重要意义。针对EEG信号的非平稳特点,本文提出了一种基于经验模式分解(EMD)和支持向量机(SVM)的EEG分类方法。首先利用EMD将EEG信号分成多个经验模式分量,然后提取有效特征,最后用SVM对EEG信号进行分类。结果表明,该方法对癫痫发作间歇期和发作期EEG的分类效果比较理想,识别率达到99%。     

Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma

Purpose

To automatically differentiate radiation necrosis from recurrent tumor at high spatial resolution using multiparametric MRI features.

Materials and Methods

MRI data retrieved from 31 patients (15 recurrent tumor and 16 radiation necrosis) who underwent chemoradiation therapy after surgical resection included post‐gadolinium T1, T2, fluid‐attenuated inversion recovery, proton density, apparent diffusion coefficient (ADC), and perfusion‐weighted imaging (PWI) ‐derived relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time maps. After alignment to post contrast T1WI, an eight‐dimensional feature vector was constructed. An one‐class‐support vector machine classifier was trained using a radiation necrosis training set. Classifier parameters were optimized based on the area under receiver operating characteristic (ROC) curve. The classifier was then tested on the full dataset.

Results

The sensitivity and specificity of optimized classifier for pseudoprogression was 89.91% and 93.72%, respectively. The area under ROC curve was 0.9439. The distribution of voxels classified as radiation necrosis was supported by the clinical interpretation of follow‐up scans for both nonprogressing and progressing test cases. The ADC map derived from diffusion‐weighted imaging and rCBV, rCBF derived from PWI were found to make a greater contribution to the discrimination than the conventional images.

Conclusion

Machine learning using multiparametric MRI features may be a promising approach to identify the distribution of radiation necrosis tissue in resected glioblastoma multiforme patients undergoing chemoradiation. J. Magn. Reson. Imaging 2011;33:296–305. © 2011 Wiley‐Liss, Inc.     

Deep learning facilitates the diagnosis of adult asthma

BackgroundWe explored whether the use of deep learning to model combinations of symptom-physical signs and objective tests, such as lung function tests and the bronchial challenge test, would improve model performance in predicting the initial diagnosis of adult asthma when compared to the conventional machine learning diagnostic method.MethodsThe data were obtained from the clinical records on prospective study of 566 adult out-patients who visited Kindai University Hospital for the first time with complaints of non-specific respiratory symptoms. Asthma was comprehensively diagnosed by specialists based on symptom-physical signs and objective tests. Model performance metrics were compared to logistic analysis, support vector machine (SVM) learning, and the deep neural network (DNN) model.ResultsFor the diagnosis of adult asthma based on symptom-physical signs alone, the accuracy of the DNN model was 0.68, whereas that for the SVM was 0.60 and for the logistic analysis was 0.65. When adult asthma was diagnosed based on symptom-physical signs, biochemical findings, lung function tests, and the bronchial challenge test, the accuracy of the DNN model increased to 0.98 and was significantly higher than the 0.82 accuracy of the SVM and the 0.94 accuracy of the logistic analysis.ConclusionsDNN is able to better facilitate diagnosing adult asthma, compared with classical machine learnings, such as logistic analysis and SVM. The deep learning models based on symptom-physical signs and objective tests appear to improve the performance for diagnosing adult asthma.     

中药寒热预测系统的建立

中药寒热性质是中药药性的重要特点之一,作者在本文着重介绍了构建的中药寒热性质预测系统,并对中药寒热药性的化学基础进行了探讨.作者建立的寒热预测系统对中药寒热药性的预测准确率,训练集达到83.3%,测试集达到81.0%.在此预测模型的基础上,提出了原子数、氢键受体数、电负性表面面积等5个化学描述符,用于解释中药寒热药性的化学基础.     

基于多尺度最大李雅普诺夫指数的表面肌电信号模式识别

为了提高动作表面肌电信号的识别率,提出一种将最大李雅普诺夫指数和多尺度分析结合的方法。从非线性和非平稳的角度出发,引入多尺度最大李雅普诺夫指数特征,并应用到人体前臂6类动作表面肌电信号的模式识别中。首先利用希尔伯特-黄变换,对原始信号进行经验模态分解,即多尺度分解;然后利用非线性时间序列分析方法,计算多尺度最大李雅普诺夫指数;最后将多尺度最大李雅普诺夫指数作为特征向量,输入支持向量机进行识别。平均识别率达到97.5%,比利用原始信号的最大李雅普诺夫指数进行识别时提高了3.9%。结果表明,利用多尺度最大李雅普诺夫指数对动作表面肌电信号进行模式识别效果良好。