1,721,208 research outputs found
Pre-Trained Lightweight Deep Learning Models for Surgical Instrument Detection: Performance Evaluation for Edge Inference
No full textSurgical instrument detection and tracking is essential in surgical procedures. The presence and proper use of surgical instruments is crucial for patient safety and overall patient outcomes. Most existing deep learning-based surgical instrument detection systems require high-end GPUs or cloud services for deployment. However, such systems are not feasible in resource-constrained or remote areas where access to expensive and bulky equipment is limited. To overcome these challenges, we propose a cost-effective and sustainable solution for deep learning based surgical instrument detection. Our approach is to detect surgical instruments from images using pretrained models on a Raspberry Pi. We compare two lightweight, pretrained deep learning models, EfficientNet and MobileNet, on their accuracy and efficiency for this task. These models are optimized for edge devices with limited computational resources. We evaluate the tradeoff between model accuracy and computational efficiency on a benchmark dataset. We discuss the strengths and limitations of each model, as well as their implications for developing surgical instrument detection applications for edge devices. This work can enable the selection of effective deep learning models for real-time inference on edge devices, and facilitate the development of efficient and cost-effective healthcare solutions
Federated Transfer Learning for Energy Efficient Privacy-preserving Medical Image Classification
No full textThe deep convolutional neural networks are widely used in medical image classification tasks. In some cases, they have outperformed physicians and achieved significant results. Unlike natural images, medical image dataset are very hard to collect, because they are protected by the privacy regulations to preserve patient's anonymity and requires a great deal of professional expertise to label them. However, because of the easier access and availability of high-performance computational resources, leveraging deep neural networks to detect diseases is becoming increasingly popular and common practice among healthcare researchers. As a result, considerable amount of energy is consumed to find an optimal and effective solution, which has a huge impact on our environment and contributes to global warming to some level. To address these challenges and reduce the carbon footprint caused by the deep learning practitioners, we attempted to combine the advantages of both federated learning and transfer learning for the medical image classification task in our study. Our findings suggest that federated transfer learning could be an useful technique to minimize computational costs and energy efficient, while maintaining privacy and addressing the problem of data scarcity. Moreover, this approach can be applied to solve other healthcare related tasks
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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