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In 1999, the Performance Improvement Committee of the Diagnostic Imaging Services of Texas Children's Hospital identified the need for smoother integration of the picture archiving and communications system (PACS) technology into ...
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In 1999, the Performance Improvement Committee of the Diagnostic Imaging Services of Texas Children's Hospital identified the need for smoother integration of the picture archiving and communications system (PACS) technology into the workflow of the rest of the department. An effort was then launched to document prevalent issues, as well as to define the processes needed to implement a department-wide program to acquaint the staff with this new technology. The department's application trainer, with the guidance of the Performance Improvement Committee, spearheaded the design and implementation of the PACS training program and has continued to develop it during the past 2 years. This article describes the format and components of the PACS training modules now in use, and details some of the positive effects of this effort.
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The Certification for Imaging Informatics Professionals (CIIP) program is sponsored by the Society of Imaging Informatics in Medicine and the American Registry of Radiologic Technologists through the American Board of Imaging Info...
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The Certification for Imaging Informatics Professionals (CIIP) program is sponsored by the Society of Imaging Informatics in Medicine and the American Registry of Radiologic Technologists through the American Board of Imaging Informatics. In 2005, a survey was conducted of radiologists, technologists, information technology specialists, corporate information officers, and radiology administrators to identify the competencies and skill set that would define a successful PACS administrator. The CIIP examination was created in 2007 in response to the need for an objective way to test for such competencies, and there have been 767 professionals who have been certified through this program to date. The validity of the psychometric integrity of the examination has been previously established. In order to further understand the impact and future direction of the CIIP certification on diplomats, a survey was conducted in 2010. This paper will discuss the results of the survey.
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Co-training under the Conditional Independence Assumption is among the models which demonstrate how radically the need for labeled data can be reduced if a huge amount of unlabeled data is available. In this paper, we explore how ...
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Co-training under the Conditional Independence Assumption is among the models which demonstrate how radically the need for labeled data can be reduced if a huge amount of unlabeled data is available. In this paper, we explore how much credit for this saving must be assigned solely to the extra assumptions underlying the Co-training Model. To this end, we compute general (almost tight) upper and lower bounds on the sample size needed to achieve the success criterion of PAC-learning in the realizable case within the model of Co-training under the Conditional Independence Assumption in a purely supervised setting. The upper bounds lie significantly below the lower bounds for PAC-learning without Co-training. Thus, Co-training saves labeled data even when not combined with unlabeled data. On the other hand, the saving is much less radical than the known savings in the semi-supervised setting.
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Current radiology training for medical students and residents predominantly consists of reviewing teaching files, attending lectures, reading textbooks and online sources, as well as one-on-one teaching at the workstation. In the ...
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Current radiology training for medical students and residents predominantly consists of reviewing teaching files, attending lectures, reading textbooks and online sources, as well as one-on-one teaching at the workstation. In the case of medical schools, radiology training is quite passive. In addition, the variety of important and high-yield cases that trainees are exposed to may be limited in scope. We utilized an open-source dcm4chee-based Picture Archiving and Communication System (PACS) named "Weasis" in order to simulate a radiologist's practice in the real world, using anonymized report-free complete cases that could easily be uploaded live during read-outs for training purposes. MySQL was used for database management and JBOSS as application server. In addition, we integrated Weasis into a web-based reporting system through Java programming language using the MyEclipse development environment. A freeware, platform-independent, image database was established to simulate a real-world PACS. The sever was implemented on a dedicated non-workstation PC connected to the hospital secure network. As the client access is through a webpage, the cases can be viewed from any computer connected to the hospital network. The reporting system allows for evaluation purposes and providing feedback to the trainees. Brief survey results are available. Implementation of such a low-cost, versatile, and customizable tool provides a new opportunity for training programs in offering medical students with an active and more realistic radiology experience, junior radiology residents with potentially better preparation for independent call, and senior resident and fellows with the ability to fine-tune high-level specialty-level knowledge.
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This article outlines the strategy used by our hospital to maximize the knowledge transfer to referring physicians on using a picture archiving and communication system (PACS). We developed an e-learning platform underpinned by th...
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This article outlines the strategy used by our hospital to maximize the knowledge transfer to referring physicians on using a picture archiving and communication system (PACS). We developed an e-learning platform underpinned by the cognitive load theory (CLT) so that in depth knowledge of PACS’ abilities becomes attainable regardless of the user’s prior experience with computers. The application of the techniques proposed by CLT optimizes the learning of the new actions necessary to obtain and manipulate radiological images. The application of cognitive load reducing techniques is explained with several examples. We discuss the need to safeguard the physicians’ main mental processes to keep the patient’s interests in focus. A holistic adoption of CLT techniques both in teaching and in configuration of information systems could be adopted to attain this goal. An overview of the advantages of this instruction method is given both on the individual and organizational level.
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We present a new and more general co-training style framework named Inter-training, to exploit unlabeled data in multi-classifier systems, and develop two concrete algorithms which employ some new strategies to iteratively retrain...
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We present a new and more general co-training style framework named Inter-training, to exploit unlabeled data in multi-classifier systems, and develop two concrete algorithms which employ some new strategies to iteratively retrain base classifiers. The decrease of diversity during iterations is a main problem which hinders the further improvement of co-training style algorithms. In this paper, we propose a method to recreate diversity among base classifiers by manipulating the pseudo-labeled data for co-training style algorithms. Furthermore, in the theoretical aspect, we define a hybrid classification and distribution (HCAD) noise and provide a Probably Approximately Correct (PAC) analysis for co-training style algorithms in the presence of HCAD noise. Experimental results on six datasets show that our method performs much better in practice, and the superiority is especially obvious on hardly-classified datasets.
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The matrix-pattern-oriented Ho-Kashyap classifier (MatMHKS), using two-sided weight vectors to constrain the matrixized samples, can deal with not only the vectorized sample but also the matrixized sample. For vectorized sample, b...
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The matrix-pattern-oriented Ho-Kashyap classifier (MatMHKS), using two-sided weight vectors to constrain the matrixized samples, can deal with not only the vectorized sample but also the matrixized sample. For vectorized sample, by converting the vectorized mode into matrixized mode, MatMHKS relieves the curse of dimensionality and extends the expressive modes of sample. Although MatMHKS has been demonstrated to be effective in the classification performance, it consumes a lot of time to alternately update two weight vectors in each iteration. Moreover, MatMHKS is not suitable in dealing with imbalanced problems. Finally, there does not exist effective analysis of generalization risk for matrixized classifiers. To this end, this paper proposes an efficient matrixized Ho-Kashyap classifier (EMatMHKS), which separately updates the two-sided weight vectors to avoid repeatedly calculating the inverse matrix in MatMHKS, thus significantly improving the training speed. Moreover, by introducing a weight matrix, both balanced and imbalanced situations can be tackled. Finally, PAC-Bayes bound is used to reflect the error upper bound of matrixized and vectorized classifiers. Both balanced and imbalanced data sets are used to validate the effectiveness and the efficiency of the proposed EMatMHKS in the experiment.
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Relational Tri-training (R-Tri-training for short), as a relational semi-supervised learning system, can effectively exploit unlabeled examples to improve the generalization ability. However, the R-Tri-training may also suffer fro...
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Relational Tri-training (R-Tri-training for short), as a relational semi-supervised learning system, can effectively exploit unlabeled examples to improve the generalization ability. However, the R-Tri-training may also suffer from the common problem in traditional semi-supervised learning, i.e., the performance is usually not stable for the unlabeled examples often be wrongly labeled and accumulated during the iterative learning process. In this paper, a new Relational Tri-training system named ADE-R-Tri-training (R-Tri-training with Adaptive Data Editing) is proposed. Not only does it employ a specific data editing technique to identify and correct the examples possibly mislabeled throughout the co-labeling iterations, but it also takes an adaptive strategy to decide whether to trigger the editing operation according to different cases. The adaptive strategy consists of five pre-conditional theorems, all of which ensure the iterative reduction of classification error under PAC (Probably Approximately Correct) learning theory. Experiments on well-known benchmarks show that ADE-R-Tri-training can more effectively enhance the performance of the hypothesis learned than R-Tri-training.
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Strength exercise improves many health outcomes in cancer survivors but the prevalence and correlates of strength exercise have not been well-described. Moreover, no study has examined the critical intention-behavior gap for exerc...
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Strength exercise improves many health outcomes in cancer survivors but the prevalence and correlates of strength exercise have not been well-described. Moreover, no study has examined the critical intention-behavior gap for exercise in cancer survivors.
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