37,818 research outputs found
Fully Automatic Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease. In this work, we propose 4 fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases. Given the similarity between the pathological impact in the lungs between Covid-19 and pneumonia, mainly during the initial stages of both lung diseases, we performed an exhaustive study of differentiation considering different pathological scenarios. To address these classification tasks, we evaluated 6 representative state-of-the-art deep network architectures on 3 different public datasets: (I) Chest X-ray dataset of the Radiological Society of North America (RSNA); (II) Covid-19 Image Data Collection; (III) SIRM dataset of the Italian Society of Medical Radiology. To validate the designed approaches, several representative experiments were performed using 6,070 chest X-ray radiographs. In general, satisfactory results were obtained from the designed approaches, reaching a global accuracy values of 0.9706 ± 0.0044, 0.9839 ± 0.0102, 0.9744 ± 0.0104 and 0.9744 ± 0.0104, respectively, thus helping the work of clinicians in the diagnosis and consequently in the early treatment of this relevant pandemic pathology.This research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research project with reference PID2019-108435RB-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, Spain through the postdoctoral grant contract ref. ED481B-2021-059; and Grupos de Referencia Competitiva, Spain, grant ref. ED431C 2020/24; Axencia Galega de Innovación (GAIN), Xunta de Galicia, Spain, grant ref. IN845D 2020/38; CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia, Spain”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014–2020, Spain, and the remaining 20% by “Secretaría Xeral de Universidades, Spain ” (Grant ED431G 2019/01). Funding for open access charge: Universidade da Coruña/CISUGXunta de Galicia; ED481B-2021-059Xunta de Galicia; ED431C 2020/24Xunta de Galicia; IN845D 2020/38Xunta de Galicia; ED431G 2019/0
Searching COVID-19 clinical research using graphical abstracts
Objective. Graphical abstracts are small graphs of concepts that visually
summarize the main findings of scientific articles. While graphical abstracts
are customarily used in scientific publications to anticipate and summarize
their main results, we propose them as a means for expressing graph searches
over existing literature. Materials and methods. We consider the COVID-19 Open
Research Dataset (CORD-19), a corpus of more than one million abstracts; each
of them is described as a graph of co-occurring ontological terms, selected
from the Unified Medical Language System (UMLS) and the Ontology of Coronavirus
Infectious Disease (CIDO). Graphical abstracts are also expressed as graphs of
ontological terms, possibly augmented by utility terms describing their
interactions (e.g., "associated with", "increases", "induces"). We build a
co-occurrence network of concepts mentioned in the corpus; we then identify the
best matches of graphical abstracts on the network. We exploit graph database
technology and shortest-path queries. Results. We build a large co-occurrence
network, consisting of 128,249 entities and 47,198,965 relationships. A
well-designed interface allows users to explore the network by formulating or
adapting queries in the form of an abstract; it produces a bibliography of
publications, globally ranked; each publication is further associated with the
specific parts of the abstract that it explains, thereby allowing the user to
understand each aspect of the matching. Discussion and Conclusion. Our approach
supports the process of scientific hypothesis formulation and evidence search;
it can be reapplied to any scientific domain, although our mastering of UMLS
makes it most suited to clinical domains.Comment: 12 pages, 6 figure
Measuring Emotions in the COVID-19 Real World Worry Dataset
The COVID-19 pandemic is having a dramatic impact on societies and economies
around the world. With various measures of lockdowns and social distancing in
place, it becomes important to understand emotional responses on a large scale.
In this paper, we present the first ground truth dataset of emotional responses
to COVID-19. We asked participants to indicate their emotions and express these
in text. This resulted in the Real World Worry Dataset of 5,000 texts (2,500
short + 2,500 long texts). Our analyses suggest that emotional responses
correlated with linguistic measures. Topic modeling further revealed that
people in the UK worry about their family and the economic situation.
Tweet-sized texts functioned as a call for solidarity, while longer texts shed
light on worries and concerns. Using predictive modeling approaches, we were
able to approximate the emotional responses of participants from text within
14% of their actual value. We encourage others to use the dataset and improve
how we can use automated methods to learn about emotional responses and worries
about an urgent problem.Comment: Accepted to ACL 2020 COVID-19 worksho
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