1,720,955 research outputs found
Deep Learning Model: Can we Ignore Foreign Objects in Chest X-ray Screening?
No full textLung (Pulmonary) abnormalities, such as Tuberculosis (TB), Asthma and/or Chronic obstructive have been global threats for years. According to the World Health Organization\u27s (WHO) report of 2019, nearly 1.5 million people have died from Tuberculosis alone. Computer scientists have worked together with medical experts to design automated screening systems for chest X-ray (CXR) images. However, in most of the research work, detection of foreign objects, such as buttons, coins, ring, pins, bone pieces and also other medical devices (like a pacemaker) hasn\u27t been considered, which have hindered the performances of automated screening system. The circular foreign objects, such as coins can often be confused with nodules, which is one of the primary indicators of Tuberculosis. Thus, for an automated screening process, we need to separate such foreign objects. This research is mainly focused on the detection of foreign objects that are of almost all shapes, sizes and texture in CXRs using a convolutional neural network. So, unlike the prior works, we will be using deep learning models, such as Faster R-CNN (Faster Region Proposal Convolutional Neural Network), to detect the foreign objects in the CXRs. That is how, instead of relying on handcrafted features, we now let the machine to extract the automated and distinguished features, to achieve minimum error possible (technically, 10^-4). Also, we localize their spatial position in CXR, so that the further process of screening can be advanced and at the same time misdiagnosis and confusion can be eliminated
Foreign Object Detection and Localization in Chest X-rays using Deep Learning
Get PDFPulmonary abnormalities, such as Tuberculosis (TB), Asthma and/or Chronic obstructive are global threats. Nearly 1.6 million died from TB alone according to the WHO (World Health Organization) report 2019. Computer scientists together with medical experts have designed and reported automated screening systems for chest X-ray (CXR) images. However, most of the research works did not consider detecting foreign objects, such as buttons, coins, ring, pins, bone pieces and other medical devices (e.g. pacemaker) all together that can hinder the performance of automatic screening system. The circle-like foreign objects, such as coins are often confused with nodules, which is one of the primary indicators of Tuberculosis. Thus, in an automated screening process foreign objects need to be separated. Unlike the previous works, we will employ deep learning models, such as Faster R-CNN (Faster Region Proposal Convolutional Neural Network), to detect almost all kinds of foreign objects in CXR images. This research is mainly focused on the detection of foreign objects that are of almost all shapes, sizes and texture in CXR using convolutional neural network. Instead of relying on handcrafted features, we now let machine to find distinguished features to achieve an error as low as possible (technically, 10^-4). We also localize their spatial position in CXR, so that the further process of screening can be advanced and at the same time misdiagnosis and confusion can be eliminated
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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