215 research outputs found
Advanced MR techniques for preoperative glioma characterization: Part 1
Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2
Advanced Sensing and Image Processing Techniques for Healthcare Applications
This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population
Advanced Image Acquisition, Processing Techniques and Applications
"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution
Analyzing Granger causality in climate data with time series classification methods
Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos
Videos represent the primary source of information for surveillance
applications and are available in large amounts but in most cases contain
little or no annotation for supervised learning. This article reviews the
state-of-the-art deep learning based methods for video anomaly detection and
categorizes them based on the type of model and criteria of detection. We also
perform simple studies to understand the different approaches and provide the
criteria of evaluation for spatio-temporal anomaly detection.Comment: 15 pages, double colum
Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging
No abstract available
Mainstream News Articles Co-Shared with Fake News Buttress Misinformation Narratives
Most prior and current research examining misinformation spread on social
media focuses on reports published by 'fake' news sources. These approaches
fail to capture another potential form of misinformation with a much larger
audience: factual news from mainstream sources ('real' news) repurposed to
promote false or misleading narratives. We operationalize narratives using an
existing unsupervised NLP technique and examine the narratives present in
misinformation content. We find that certain articles from reliable outlets are
shared by a disproportionate number of users who also shared fake news on
Twitter. We consider these 'real' news articles to be co-shared with fake news.
We show that co-shared articles contain existing misinformation narratives at a
significantly higher rate than articles from the same reliable outlets that are
not co-shared with fake news. This holds true even when articles are chosen
following strict criteria of reliability for the outlets and after accounting
for the alternative explanation of partisan curation of articles. For example,
we observe that a recent article published by The Washington Post titled
"Vaccinated people now make up a majority of COVID deaths" was
disproportionately shared by Twitter users with a history of sharing
anti-vaccine false news reports. Our findings suggest a strategic repurposing
of mainstream news by conveyors of misinformation as a way to enhance the reach
and persuasiveness of misleading narratives. We also conduct a comprehensive
case study to help highlight how such repurposing can happen on Twitter as a
consequence of the inclusion of particular narratives in the framing of
mainstream news
- …