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18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)最新文献

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Classification of the auditory brainstem response (ABR) using wavelet analysis and Bayesian network 基于小波分析和贝叶斯网络的听觉脑干反应分类
18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05) Pub Date : 2005-06-23 DOI: 10.1109/CBMS.2005.41
Rui Zhang, Gerry McAllister, B. Scotney, S. McClean, G. Houston
{"title":"Classification of the auditory brainstem response (ABR) using wavelet analysis and Bayesian network","authors":"Rui Zhang, Gerry McAllister, B. Scotney, S. McClean, G. Houston","doi":"10.1109/CBMS.2005.41","DOIUrl":"https://doi.org/10.1109/CBMS.2005.41","url":null,"abstract":"The auditory brainstem response (ABR) has become a routine clinical tool for hearing and neurological assessment. In order to pick out the ABR from the background EEG activity that obscures it, stimulus-synchronized averaging of many repeated trials is necessary and it typically requires up to 2000 repetitions. This number of repetitions can be very difficult, time consuming and uncomfortable for some subjects. In this study a method combining the wavelet analysis and the Bayesian network is introduced to reduce the required number of repetitions, which could offer a great advantage in the clinical situation. The important features of the ABR are extracted by thresholding and matching the wavelet coefficients. These extracted features are then used as the variables to build up the Bayesian network for classifying the ABR. 172 ABRs with 64 repetitions are applied in this study to learn the Bayesian network and estimate the conditional probability tables (CPTs). A further 142 ABRs with 64 repetitions are used to test the network. Moreover, this Bayesian network can also be applied to classify the ABRs with 128 repetitions.","PeriodicalId":119367,"journal":{"name":"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127439014","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: 7
A novel computational analysis of heterogeneity in breast tissue 一种新的乳腺组织异质性计算分析
18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05) Pub Date : 2005-06-23 DOI: 10.1109/CBMS.2005.16
S. Maskery, Yonghong Zhang, R. Jordan, Hai Hu, C. Shriver, J. Hooke, M. Liebman
{"title":"A novel computational analysis of heterogeneity in breast tissue","authors":"S. Maskery, Yonghong Zhang, R. Jordan, Hai Hu, C. Shriver, J. Hooke, M. Liebman","doi":"10.1109/CBMS.2005.16","DOIUrl":"https://doi.org/10.1109/CBMS.2005.16","url":null,"abstract":"Breast cancer presents as part of a heterogeneous mix of breast disease pathologies whose biological origins are poorly understood. A systematic and quantitative study of heterogeneity in breast tissue would enable us to characterize the disease states present, and use that characterization to guide further research into the complex pathologic associations within breast tissue and between patients. Initially we focus on characterizing the co-occurrence of breast pathology-related diagnoses. In particular, this abstract presents our initial results from characterizing the co-occurrence of double and triple diagnoses. We will expand this analysis to co-occurrence of larger diagnosis sets. Additionally, we plan to analyze co-occurrence with other types of patient information, including: socio-economic status, family history, lifestyle choices, co-morbidity with other diseases, and many other factors hypothesized to contribute to an increased risk for developing breast cancer.","PeriodicalId":119367,"journal":{"name":"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127258865","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: 1
Relevance feedback for spine X-ray retrieval 脊柱x线检索的相关反馈
18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05) Pub Date : 2005-06-23 DOI: 10.1109/CBMS.2005.94
Xiaoqian Xu, D. J. Lee, Sameer Kiran Antani, L. Long
{"title":"Relevance feedback for spine X-ray retrieval","authors":"Xiaoqian Xu, D. J. Lee, Sameer Kiran Antani, L. Long","doi":"10.1109/CBMS.2005.94","DOIUrl":"https://doi.org/10.1109/CBMS.2005.94","url":null,"abstract":"Relevance feedback (RF) has been an active research area in content-based image retrieval (CBIR). RF intends to bridge the gap between the low-level image features and the high-level human visual perception by analyzing and employing the feedback information provided by the user. This gap becomes more evident and important in medical image retrieval due to the two distinct facts with regard to medical images: (1) subtle differences between images, even between pathological and non-pathological images; (2) subjective and different diagnosis even among experts. This paper describes a novel linear weight-updating approach for RF applying to spine X-ray image retrieval. The algorithm utilizes both positive and negative examples to gain feedback from the user. Experimental results show that the proposed approach can substantially improve the retrieval performance to better satisfy the individual user's preferences.","PeriodicalId":119367,"journal":{"name":"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)","volume":"09 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127219536","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: 16
Validating health status questionnaires in medicine: examples from a real life trial 医学中健康状况问卷的验证:来自真实生活试验的例子
18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05) Pub Date : 2005-06-23 DOI: 10.1109/CBMS.2005.103
Margaret G. E. Peterson, L. Robbins, Nancy Kwong
{"title":"Validating health status questionnaires in medicine: examples from a real life trial","authors":"Margaret G. E. Peterson, L. Robbins, Nancy Kwong","doi":"10.1109/CBMS.2005.103","DOIUrl":"https://doi.org/10.1109/CBMS.2005.103","url":null,"abstract":"In this paper, a project that required only one set of data entry is reported. In this, each participant response is entered into a record similar to an Excel record with the variable values reading across horizontally and is identified by the study number of the participant, the participant's group (exerciser or control), and the number designating the time point in the study. This matrix sufficed for most of the reliability analysis, and for the calculation of intra-class correlation coefficients. These records could then be merged horizontally by study number to do repeated measures analysis and to calculate Cohen's kappa.","PeriodicalId":119367,"journal":{"name":"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130827771","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: 0
Data mining methods supporting diagnosis of melanoma 支持黑色素瘤诊断的数据挖掘方法
18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05) Pub Date : 2005-06-23 DOI: 10.1109/CBMS.2005.46
J. Grzymala-Busse, Z. Hippe
{"title":"Data mining methods supporting diagnosis of melanoma","authors":"J. Grzymala-Busse, Z. Hippe","doi":"10.1109/CBMS.2005.46","DOIUrl":"https://doi.org/10.1109/CBMS.2005.46","url":null,"abstract":"Melanoma, a dangerous skin cancer, is usually diagnosed using the ABCD formula. The main objective of our research was to find a better formula resembling the original ABCD formula using four different discretization methods. All four corresponding modified ABCD formulas are significantly more accurate (with the level of significance 5%) than the original ABCD formula. Our additional objective was to calibrate the rule set induced from the original data set, describing melanoma, using the best discretization method, so that the sensitivity (the conditional probability for recognition of malignant and suspicious melanoma) was increased. This objective was accomplished using a technique of changing rule strengths.","PeriodicalId":119367,"journal":{"name":"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133959893","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: 14
Approximations to magic: finding unusual medical time series 近似魔法:发现不寻常的医疗时间序列
18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05) Pub Date : 2005-06-23 DOI: 10.1109/CBMS.2005.34
Jessica Lin, Eamonn J. Keogh, A. Fu, H. V. Herle
{"title":"Approximations to magic: finding unusual medical time series","authors":"Jessica Lin, Eamonn J. Keogh, A. Fu, H. V. Herle","doi":"10.1109/CBMS.2005.34","DOIUrl":"https://doi.org/10.1109/CBMS.2005.34","url":null,"abstract":"In this work we introduce the new problem of finding time series discords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. While the brute force algorithm to discover time series discords is quadratic in the length of the time series, we show a simple algorithm that is 3 to 4 orders of magnitude faster than brute force, while guaranteed to produce identical results.","PeriodicalId":119367,"journal":{"name":"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133463130","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: 138
A data mining based approach for the EEG transient event detection and classification 基于数据挖掘的脑电瞬态事件检测与分类方法
18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05) Pub Date : 2005-06-23 DOI: 10.1109/CBMS.2005.7
T. Exarchos, A. Tzallas, D. Fotiadis, S. Konitsiotis, S. Giannopoulos
{"title":"A data mining based approach for the EEG transient event detection and classification","authors":"T. Exarchos, A. Tzallas, D. Fotiadis, S. Konitsiotis, S. Giannopoulos","doi":"10.1109/CBMS.2005.7","DOIUrl":"https://doi.org/10.1109/CBMS.2005.7","url":null,"abstract":"An automated methodology which detects transient events in EEG recordings and classifies those as epileptic spikes, muscle activity, eye blinking activity and sharp alpha activity is presented. It is based on data mining algorithms and includes four stages: (I) EEG preprocessing and transient events detection, (II) clustering of transient events and feature extraction, (III) feature discretization and (IV) association rule mining and classification. The methodology is evaluated using a dataset of 25 EEG recordings and the obtained overall accuracy is 84.35%. The major advantage of our approach is that it is able to provide interpretation for the decisions made since it is based on a set of association rules.","PeriodicalId":119367,"journal":{"name":"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134231902","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: 14
Reducing the computational cost for statistical medical image analysis: an MRI study on the sexual morphological differentiation of the corpus callosum 减少统计医学图像分析的计算成本:胼胝体性别形态分化的MRI研究
18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05) Pub Date : 2005-06-23 DOI: 10.1109/CBMS.2005.93
D. Kontos, V. Megalooikonomou, J. Gee
{"title":"Reducing the computational cost for statistical medical image analysis: an MRI study on the sexual morphological differentiation of the corpus callosum","authors":"D. Kontos, V. Megalooikonomou, J. Gee","doi":"10.1109/CBMS.2005.93","DOIUrl":"https://doi.org/10.1109/CBMS.2005.93","url":null,"abstract":"We illustrate the application of intelligent medical image analysis techniques in order to reduce the computational cost of statistical voxel-wise analysis for detecting discriminative regions of morphological variability among different populations. We demonstrate that novel statistical image processing techniques that operate selectively on groups of pixels are suitable for morphological analysis of anatomical structures visualized by modern medical imaging modalities. We also show that the proposed methodology effectively decreases the number of statistical tests performed, alleviating the effect of the multiple comparison problem. We show that our approach detects regions of statistically significant morphological variability. Our results validate previous findings, while being robust across a wide range of experimental settings.","PeriodicalId":119367,"journal":{"name":"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130506486","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
Incremental learning of ensemble classifiers on ECG data 心电数据集成分类器的增量学习
18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05) Pub Date : 2005-06-23 DOI: 10.1109/CBMS.2005.69
Jan Macek
{"title":"Incremental learning of ensemble classifiers on ECG data","authors":"Jan Macek","doi":"10.1109/CBMS.2005.69","DOIUrl":"https://doi.org/10.1109/CBMS.2005.69","url":null,"abstract":"We develop novel methods of incremental learning based on the bagging and boosting approaches to ensemble learning. Our method combines perceptron decision trees obtained with a margin maximizing algorithm into an ensemble in an incremental way. We demonstrate practical functionality of our algorithm on the task of ECG records classification. Our results are promising since comparable or superior accuracy is achieved when compared with results obtained by other existing methods of classification of ECG records, namely with the C5.0 decision tree algorithm.","PeriodicalId":119367,"journal":{"name":"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113965512","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: 19
Preprocessing of mass spectrometry proteomics data on the grid 网格上质谱蛋白质组学数据的预处理
18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05) Pub Date : 2005-06-23 DOI: 10.1109/CBMS.2005.87
M. Cannataro, P. Guzzi, T. Mazza, G. Tradigo, P. Veltri
{"title":"Preprocessing of mass spectrometry proteomics data on the grid","authors":"M. Cannataro, P. Guzzi, T. Mazza, G. Tradigo, P. Veltri","doi":"10.1109/CBMS.2005.87","DOIUrl":"https://doi.org/10.1109/CBMS.2005.87","url":null,"abstract":"The combined use of mass spectrometry and data mining is a novel approach in proteomic pattern analysis for discovering novel biomarkers or identifying patterns and associations in proteomic profiles. Data produced by mass spectrometers are affected by errors and noise due to sample preparation and instrument approximation, so different preprocessing techniques need to be applied before analysis is conducted. We survey different techniques for spectra preprocessing, and we present a first design of a software tool that allows the preprocessing, management and analysis of mass spectrometry data on the Grid.","PeriodicalId":119367,"journal":{"name":"18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122628555","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: 33
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