In der vorliegenden Arbeit wurde untersucht, ob die farbige DSA-Darstellungsweise besser als die monochromatische dazu geeignet ist, eine Entscheidung über eine mögliche Stentimplantation bei pAVK zu treffen. Hierfür wurden DSA-Daten des Universitätsklinikums Würzburg im Zeitraum 04/2014 - 10/2015 retrospektiv ausgewertet. Drei Ärzte bewerteten die Bilder in zwei getrennten Durchgängen bezüglich ihrer Entscheidung zur Stentimplantation. Diese Entscheidungen wurden mit einem Konsensus aus 2 Ärzten verglichen. Anhand von ROC-Analysen konnte so die Treffsicherheit der Entscheidungen evaluiert werden. In der Studie stellte sich die farbkodierte Darstellung im Vergleich zur monochromatischen Darstellung nicht als überlegen heraus. / The present paper analyses if color-coded DSA is more suitable than conventional monochromatitic DSA to decide on a possible stent implantation for a pAVK-lesion. Therefor DSA-Data of the University Hospital of Würzburg was evaluated retrospectively of the period from 04/2014 – 10/2015. Three doctors assessed the pictures in two separate cycles in terms of their decision concerning stenting. These decisions were compared with a consensus of two doctors. The accuracy of the decisions were evaluated based on ROC-analyses. In this study, color coded DSA did not prove to be superior to conventional DSA.

This thesis tests the relative efficiency of Fischer's approach of Chi-square on Affinity viewing time profiles to the more commonly used deviance differential approach. Through his use of a Chi-square approach, Fischer has attempted to make a norm-referenced, comparison of ipsative scores generated using Affinity. His goal has been to create an ethically acceptable approach to identifying sexual interest without losing the efficiency generated through the use of the commonly used deviance differential. A Receiver Operating Characteristic (ROC) curve is used to generate the efficiency of each approach and provide results for comparison. These results lead to a discussion of their implications for Fischer's approach of Chi-square, the deviance differential approach, and to the general use of data generated by Affinity.

The aim of this thesis is to investigate the effects of JPEG2000 compression on texture feature extraction from digitized mammograms. A partially automated computer aided diagnosis system is designed, implemented, and tested for this analysis. The system is tested on a database of 60 digital mammograms obtained from the Digital Database for Screening Mammography at the University of South Florida. Using JPEG2000, the mammograms are compressed at 20 different compression ratios ranging from 17:1 to 10,000:1. Two approaches to texture feature extraction are investigated: (i) region of interest (ROI), which is a bounding box around the segmented mass and (ii) rubber band straightening transform (RBST), which is a band of pixels around the segmented mass transformed to a rectangular strip. The gray tone spatial dependent matrices are computed from the ROI and the RBST for the original uncompressed mammograms as well as each group of compressed images. Feature selection and optimization is achieved via stepwise linear discriminant analysis. The efficacy of the features is measured using receiver operator characteristic (ROC) curves. The efficacy of the texture features obtained from the original mammograms is compared to those of the compressed mammograms. Overall, the texture feature efficacy was preserved even for relatively high compression ratios. For example, the area under the ROC curve was greater than 0.99 for compression ratios as high as 5000:1, when the RBST method was utilized. Overall, the JPEG2000 compression distorted the RBST texture features lesser than the ROI texture features.

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No description available.

<p> The accuracy of a diagnostic test is often evaluated with the measures of sensitivity and specificity and the joint dependence between these two measures is captured by the receiver operating characteristic (ROC) curve. To combine multiple testing results from studies that are assumed to follow the same underlying probability law, a smooth summary receiver operating characteristic (SROC) curve can be fitted. Moses et al. (1993) proposed a least squares approach to fit the smooth SROC curve. </p> <p> In this thesis we overview the summary measures for the ROC curve in single study data as well as the summary statistics for the SROC curves in meta-analysis. These summary statistics include, the area under the curve (AUC), Q* statistic, area swept under the curve (ASC) and the partial area under the curve (pAUC). </p> <p> Our focus, however is mainly on the partial area under the SROC curve as it is being used frequently in meta-analysis of diagnostic testing. The appeal to use the pAUC instead of the full AUC is that the partial area can be used to focus on a clinically relevant region of the SROC curve where false positive rate (FPR) is small. Simulations and considerations for the use of the summary indices of the ROC and SROC curves are presented here. </p> / Thesis / Master of Science (MSc)

The Receiver Operating Characteristic (ROC) curve is used to measure the classification accuracy of tests that yield ordinal or continuous scores. Ordinal scores are common in medical imaging studies and, more recently, in black-box studies on forensic identification accuracy (Phillips et al., 2018). To assess the accuracy of radiologists in medical imaging studies or the accuracy of forensic examiners in biometric studies, one needs to estimate the ROC curves from the ordinal scores and account for the covariates related to the radiologists or forensic examiners. In this thesis, we propose a homogeneity test to compare the performance of raters. We derive the asymptotic properties of estimated ROC curves and their corresponding Area Under the Curve (AUC) within an ordinal regression framework. Moreover, we investigate differences in ROC curves (and AUCs) among examiners in detail. We construct confidence intervals for the difference in AUCs and confidence bands for the difference in ROC curves for performance comparison purposes. First, we conduct simulations on data where scores are assumed to be normally distributed, and the features include both categorical and continuous covariates. Then, we apply our procedure to facial recognition data to compare forensic examiners. The second part of this thesis addresses the correlation of decision scores among raters. In medical imaging studies and facial recognition, multiple raters assess the same subject pairs, leading to potential score correlations. Because of these correlated scores, standard methods for generalized linear models cannot be directly applied to estimate accuracy. In this thesis, we employ the generalized estimating equation to estimate covariate-specific and covariate-adjusted AUC values when correlations are present in ordinal scores. We conduct homogeneity tests on both covariate-specific and covariate-adjusted AUCs, investigating their statistical properties. To assess the finite sample properties of the test, we conduct simulation studies. Furthermore, we apply this test to real facial recognition data.

This is a foundational study that applies Receiver Operating Characteristic (ROC) analysis to the evaluation of a chronic disease model that utilizes Remote Monitoring (RM) devices to identify clinical deterioration in a Chronic Obstructive Pulmonary Disease (COPD) population. Background: RM programmes in Disease Management (DM) are proliferating as one strategy to address management of chronic disease. The need to validate and quantify evidence-based value is acute. There is a need to apply new methods to better evaluate automated RM systems. ROC analysis is an engineering approach that has been widely applied to medical programmes but has not been applied to RM systems. Evaluation of classifiers, determination of thresholds and predictive accuracy for RM systems have not been evaluated using ROC analysis. Objectives: (1) apply ROC analysis to evaluation of a RM system; (2) analyse the performance of the model when applied to patient outcomes for a COPD population; (3) identify predictive classifier(s); (4) identify optimal threshold(s) and the predictive capacity of the classifiers. Methods: Parametric and non-parametric methods are utilized to determine accuracy, sensitivity, specificity and predictive capacity of classifiers Saturated Peripheral Oxygen (SpO2), Blood Pressure (BP), Pulse Rate (PR) based on event-based patient outcomes that include hospitalisation (IP), accident & emergency (A&E) and home visits (HH). Population: Patients identified with a primary diagnosis of COPD, monitored for a minimum of 183 days with at least one episode of in-patient (IP) hospitalisation for COPD in the 12 months preceding the monitoring period. Data Source: A subset of retrospective de-identified patient data from an NHS Direct evaluation of a COPD RM programme. Subsets utilized include classifiers, biometric readings, alerts generated by the system and resource utilisation. Contribution: Validates ROC methodology, identifies classifier performance and optimal threshold settings for the classifier, while making design recommendations and putting forth the next steps for research. The question answered by this research is that ROC analysis can provide additional information on the predictive capacity of RM systems. Justification of benefit: The results can be applied when evaluating health services and planning decisions on the costs and benefits. Methods can be applied to system design, protocol development, work flows and commissioning decisions based on value and benefit. Conclusion: Results validate the use of ROC analysis as a robust methodology for DM programmes that use RM devices to evaluate classifiers, thresholds and identification of the predictive capacity as well as identify areas where additional design may improve the predictive capacity of the model.

Submitted by Adriana Alves Rodrigues (aalves@espm.br) on 2018-10-11T13:56:04Z No. of bitstreams: 1 MPPJM - RICARDO FOTIOS HATZIGEROGIOU.pdf: 89994916 bytes, checksum: 10eec05217008402d932b74a6a5896c0 (MD5) / Approved for entry into archive by Adriana Alves Rodrigues (aalves@espm.br) on 2018-10-11T13:56:34Z (GMT) No. of bitstreams: 1 MPPJM - RICARDO FOTIOS HATZIGEROGIOU.pdf: 89994916 bytes, checksum: 10eec05217008402d932b74a6a5896c0 (MD5) / Approved for entry into archive by Debora Cristina Bonfim Aquarone (deborabonfim@espm.br) on 2018-10-11T13:58:17Z (GMT) No. of bitstreams: 1 MPPJM - RICARDO FOTIOS HATZIGEROGIOU.pdf: 89994916 bytes, checksum: 10eec05217008402d932b74a6a5896c0 (MD5) / Made available in DSpace on 2018-10-11T13:58:29Z (GMT). No. of bitstreams: 1 MPPJM - RICARDO FOTIOS HATZIGEROGIOU.pdf: 89994916 bytes, checksum: 10eec05217008402d932b74a6a5896c0 (MD5) Previous issue date: 2018-03-22 / The fragmentation of the production and consumption of news on the Internet triggers the need for more and better processes to measure audience habits to guide the journalistic work. This new skill in the professional routine – which emerges from the increase in “big data” gathering and analyzes associated with the complexity of the publishing systems, also called CMS (Content Management System) – allows the journalist to follow all stages of news spreading in terms of quantity of accesses as well as public engagement. In this research, called ROC (Portuguese acronym for Click-Oriented News Reporting), we sought to analyze the impact that the knowledge of these consumption habits exerts on the deontology of cyberjournalism, more specifically, on the selection of guidelines and highlights in editorial homepages of websites and news portals. / A fragmentação da produção e do consumo de notícias na internet desencadeia a necessidade de mais e melhores processos de aferição de métricas de audiência para balizar o trabalho jornalístico. Essa nova competência na rotina profissional – que emerge da expansão dos cruzamentos de dados em rede (big data) associada à complexificação dos sistemas publicadores, chamados CMS (Content Management System) – permite ao jornalista acompanhar a trajetória da circulação da produção informativa tanto em quantidade de acessos quanto em engajamento do público. Nesta pesquisa, denominada Reportagem Orientada pelo Clique (ROC), buscamos analisar o impacto que o conhecimento dos hábitos de consumo exerce sobre a deontologia do ciberjornalismo, mais especificamente, na escolha de pautas e na seleção de destaques nas homepages editoriais de sites e portais de notícias.

新版巴賽爾資本協定的內部評等法中，銀行可自行對借貸戶進行評分，並且根據 評分估算信用風險以提領準備金，因此估算借貸戶評分分數的違約機率(PD)是相當 重要的一環。過去估算違約機率的研究中，大多假定評分分數為離散型式，本文針對 評分分數為連續形式時，提出一種利用曲線函數來配適估計模型。估計模型是使用伽 瑪的截尾分配去配適ROC曲線函數，再利用此ROC曲線函數來估計各評分分數下的 違約機率P(D|S)，在伽瑪分配中的兩參數則是用兩階段的方法求解。本文所提的估 計方法並無假設評分分數的分配，因此在數值方法中使用不同的分配、參數設定、違 約機率等，來驗證此方法的準確度與穩定度，並且與Van der Burgt (2008)、Tasche(2009)的估計方法比較。 / By the internal rating-based approach of Basel II, banks estimate borrowers' default risks to withdraw reserves independently. Hence, estimating default probability (PD) of borrowers is important. Most of previous studies estimating PD assume that evaluation scores are discrete, In this study, we use curve function to t estimation model in the condition that the evaluation scores are continuous . We use truncated gamma distribution to t ROC curve function. And we use the ROC curve function to estimate PD of dierent scores. And use two-step method to nd the value of two parameters in gamma distribution. The estimation method in this study doesn't assume the distribution of estimation scores,so we use dierent distributions, parameters, and default probabilities to test the accuracy and stability of this method. In the end, we also compare our methods with Van der Burgt (2008) and Tasche (2009)' methods.