1,721,047 research outputs found
198 Reliable and interpretable segmentation for remote assessment of atopic dermatitis severity using digital images
No full textAssessing the severity of atopic dermatitis (AD) traditionally relies on a face-to-face assessment by healthcare professionals and may suffer from inter- and intra-rater variability. With the increasing demand for better patient self-care and the growing popularity of telemedicine post-pandemic, the ability to assess AD severity remotely from digital images, such as those taken with a smartphone camera, is becoming increasingly important. Previously, we developed EczemaNet, a fully automated computer vision pipeline for detecting and assessing AD severity. It demonstrated good performance for assessing AD severity in real-world images while being robust to suboptimal imaging conditions. However, we recognize that it had limitations in practical use due to its lack of interpretability in AD area segmentation and to the need for more reliable AD segmentation data provided by specialists. At the same time, we also found a poor agreement for AD segmentation in digital images, with the average interclass correlation coefficient among four dermatologists being 0.45 on 80 digital images. To address these challenges, we improved EczemaNet pipeline to perform AD segmentation in a more reliable and interpretable fashion. The new pipeline uses pixel-level segmentation and data augmentation to improve the quality and robustness of AD lesion detection. We achieved pixel-level AD segmentation using U-Net architecture and evaluated the reliability of the pipeline using various data augmentation methods such as Pix2Pix. Our investigation found that the use of whole-skin images for model training is a viable alternative as a data collection strategy, which would allow the data acquisition to be more cost-effective without affecting the system's final performance
129 Fully automated assessment of Atopic Dermatitis severity from real-world digital images
No full textAssessing the severity of Atopic Dermatitis (AD) traditionally relies on face-to-face assessments by healthcare professionals. Such approaches are resource-intensive for participants and staff, challenging during pandemics, and prone to inter- and intra-observer variation. We aim to investigate to what extent computer vision algorithms can help standardise and automate the detection and assessment of AD severity using real-world digital images, without human intervention. We developed EczemaNet, a deep learning computer vision pipeline to detect and assess AD severity from digital camera images. We first trained a model that can detect AD lesions in images using the data provided by four dermatologists who delineated (“segmented”) AD regions in 1345 images from 287 children. We then trained a second model that can assess seven AD disease signs from the AD regions identified. EczemaNet demonstrated good performance for assessing the AD severity in real-world images, while being robust to poor imaging conditions. We noted poor inter-rater reliability in the segmentation of AD regions by dermatologists, i.e. dermatologists rarely reached a consensus on the location of AD lesions in the images. We demonstrated the potential of deep learning for assessing AD severity from digital camera images. Nevertheless, we highlighted the challenge of accurately detecting AD lesions. It may limit the performance of algorithms attempting to assess AD severity from real-world camera images
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Full text linkWe 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
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