2016 8th International Conference on Information Technology in Medicine and Education (ITME)最新文献_第10页

Optimizing Correlation Measure Based Exploratory Analysis 基于探索性分析的相关测度优化

2016 8th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2016-12-01 DOI: 10.1109/ITME.2016.0149

Shijia Hao, Hui Li, Xiaoping Zhang, Mei Chen, Ming-yi Zhu

{"title":"Optimizing Correlation Measure Based Exploratory Analysis","authors":"Shijia Hao, Hui Li, Xiaoping Zhang, Mei Chen, Ming-yi Zhu","doi":"10.1109/ITME.2016.0149","DOIUrl":"https://doi.org/10.1109/ITME.2016.0149","url":null,"abstract":"Exploratory data analysis refers to the existing data to explore under the assumption of less as far as possible, through drawing, tabulation, calculation methods to explore characteristics of data structure and regularity of a kind of analysis method. However, exploratory data by calculation method is a very general method to find the key of data. In this paper, we introduce a correlation measure for exploratory analysis based on maximal information coefficient. First, we briefly introduce the traditional data analysis methods and features, expound the necessity of exploring analysis and content. Then, correlation measurement which used commonly are expounded, summarized their characteristics and models. Therefore, we propose a weighted measure based on Maximal Information Coefficient to improve effectiveness of exploratory analysis. Then we get eigenvalues of the Maximal Information Coefficient and Pearson correlation coefficient in linear and nonlinear function of plus noise. Finally, explore analysis display visualization of the results by test dataset, emphasis direction of further research.","PeriodicalId":184905,"journal":{"name":"2016 8th International Conference on Information Technology in Medicine and Education (ITME)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117129574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Survey for Information Technology Based Roll Call Methods in University Education 高校教育中基于信息技术的点名方法研究

2016 8th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2016-12-01 DOI: 10.1109/ITME.2016.0098

Tigang Jiang, Xiaodan Zheng

引用次数: 0

Segmentation of the Aortic Dissection from CT Images Based on Spatial Continuity Prior Model 基于空间连续性先验模型的CT主动脉夹层分割

2016 8th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2016-12-01 DOI: 10.1109/ITME.2016.0069

Xiaojie Duan, Meichen Shi, Jianming Wang, Zhao He, Dandan Chen

引用次数: 13

Automated Quantitative Image Analysis of Hematoxylin-Eosin Staining Slides in Lymphoma Based on Hierarchical Kmeans Clustering 基于层次Kmeans聚类的淋巴瘤苏木精-伊红染色切片自动定量图像分析

2016 8th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2016-12-01 DOI: 10.1109/ITME.2016.0031

Peng Shi, Jing Zhong, Rongfang Huang, Jian-Jiao Lin

{"title":"Automated Quantitative Image Analysis of Hematoxylin-Eosin Staining Slides in Lymphoma Based on Hierarchical Kmeans Clustering","authors":"Peng Shi, Jing Zhong, Rongfang Huang, Jian-Jiao Lin","doi":"10.1109/ITME.2016.0031","DOIUrl":"https://doi.org/10.1109/ITME.2016.0031","url":null,"abstract":"The microscopic image of tissue section stained by hematoxylin-eosin (HE) is an essential part in histopathology researches. Automated HE image processing remains challenging because forms and distributions of cells and other tissue structures are always extremely irregular with no clear boundaries, especially in conducting high throughput analysis which demands higher accuracy and efficient quantification for the reference of pathologists. To solve this problem, we proposed an automated quantitative image analysis pipeline based on hierarchical clustering of local correlations, which segmented the image into nuclei, cytoplasm and extracellular spaces by classifying image pixels on the basis of local correlation features. Segmentation for precise nucleus boundaries was then performed, and finally a set of indicators characterizing tissue structures were extracted to complete quantification of HE images. Experimental results showed high accuracy and adaptability in cell segmentation despite data variance. Quantitative indicators obtained in this essay provide a reliable evidence for the analysis of HE staining lymphoma pathological image.","PeriodicalId":184905,"journal":{"name":"2016 8th International Conference on Information Technology in Medicine and Education (ITME)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131459244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Ultrasound Medical Image Denoising Based on Multi-direction Median Filter 基于多方向中值滤波的超声医学图像去噪

2016 8th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2016-12-01 DOI: 10.1109/ITME.2016.0194

Xiaofeng Zhang, Shih-Sian Cheng, Hong Ding, Huiqun Wu, Nianmei Gong, Rengui Cheng

引用次数: 10

An Accurate Passive Shimming Method 一种精确的被动摆振方法

2016 8th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2016-12-01 DOI: 10.1109/ITME.2016.0024

Wenju Tang, Hui Wang, Yi Li, Xuchen Zhu, Chaoqun Niu, Yang Hu, Mingda Gao

引用次数: 2

Multi-combined Features Text Mining of TCM Medical Cases with CRF 基于CRF的中医案例多组合特征文本挖掘

2016 8th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2016-12-01 DOI: 10.1109/ITME.2016.0146

Qi-yu Jiang, Hongyi Li, Jiafen Liang, Qing-Xiang Wang, Xiao-Mu Luo, Hui-Ling Liu

{"title":"Multi-combined Features Text Mining of TCM Medical Cases with CRF","authors":"Qi-yu Jiang, Hongyi Li, Jiafen Liang, Qing-Xiang Wang, Xiao-Mu Luo, Hui-Ling Liu","doi":"10.1109/ITME.2016.0146","DOIUrl":"https://doi.org/10.1109/ITME.2016.0146","url":null,"abstract":"TCM medical cases in records are free text with much valuable data and clinical terms, how to recognize and extract these clinical terms automatically is a valuable work. TCM medical records obtained from Guangdong Provincial Hospital of Chinese Medicine are segmented to single word and labeled with five labeling features(words in sentence, grammatical property of words, words in clinical dictionary, set phrases acting on neighbor context, and set phrases acting on far distance.), and divided into training sets and testing sets. Training sets are also handled with outputted labeling (labeling of symptoms or signs, TCM diagnosis, TCM syndrome type, Chinese medicines (drug), and Names of TCM prescriptions.). In order to evaluate abilities of labeling features on improving clinical terms recognition with CRF, three indicators (recognition Precision (P), recognition Recall (R) and F-score (F)) are defined, and three comparisons are given: comparisons of individual labeling features, comparisons of combined labeling features, and comparisons of combined features in different diseases. The results show that, \"grammatical property of words\" is the best labeling features in all individual labeling features. Multi-combined features have higher scores than individual labeling features on improving clinical terms recognition. The combined mode of \"grammatical property of words\", \"words in sentence\", and \"words in clinical dictionary\" may be the most suitable labeling features. Multi-combined labeling features can improve term recognition with CRF model for text mining in TCM medical cases.","PeriodicalId":184905,"journal":{"name":"2016 8th International Conference on Information Technology in Medicine and Education (ITME)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127535781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Lesion Recognition Method of Liver CT Images Based on Random Forest 基于随机森林的肝脏CT图像病变识别方法

2016 8th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2016-12-01 DOI: 10.1109/ITME.2016.0058

Xinyu Jin, Ting Zhang, Lanjuan Li, Haitao Wu, Bin Sun

引用次数: 4

Experimental Case Design of “Mobile Device Programming” for Specialty of Information Management and Information System 信息管理与信息系统专业“移动设备编程”实验案例设计

2016 8th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2016-12-01 DOI: 10.1109/ITME.2016.0128

Yang Xiao

引用次数: 0

A Cost Sensitive and Class-Imbalance Classification Method Based on Neural Network for Disease Diagnosis 基于神经网络的成本敏感类不平衡分类方法在疾病诊断中的应用

2016 8th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2016-12-01 DOI: 10.1109/ITME.2016.0012

Fei He, Huamin Yang, Y. Miao, Rainbow Louis

引用次数: 3