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| http://dx.doi.org/10.3389/fnins.2021.732165 | DOI Listing |
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Dynamic-budget superpixel active learning for semantic segmentation.
Front Artif Intell
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Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
Introduction: Active learning can significantly decrease the labeling cost of deep learning workflows by prioritizing the limited labeling budget to high-impact data points that have the highest positive impact on model accuracy. Active learning is especially useful for semantic segmentation tasks where we can selectively label only a few high-impact regions within these high-impact images. Most established regional active learning algorithms deploy a static-budget querying strategy where a fixed percentage of regions are queried in each image.
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Department of Nephrology, The Scarborough Health Network, Toronto, Ontario, Canada; Kidney Life Sciences Institute, Toronto, Ontario, Canada.
Managing diabetes in patients on peritoneal dialysis (PD) is challenging due to the combined effects of dietary glucose, glucose from dialysate, and other medical complications. Advances in technology that enable continuous biological data collection are transforming traditional management approaches. This review explores how multi-omics technologies and artificial intelligence (AI) are enhancing glucose management in this patient population.
View Article and Find Full Text PDFBIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches.
Front Genet
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Department of Mechatronics Engineering, Parul Institute of Technology, Parul University, Vadodara, Gujarat, India.
Background: Cancer rates are rising rapidly, causing global mortality. According to the World Health Organization (WHO), 9.9 million people died from cancer in 2020.
View Article and Find Full Text PDFMulti-cohort analysis identifying core ocular surface microbiome and bacterial alterations in eye diseases.
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Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
Purpose: Inconsistency exists among reported studies on the composition of human ocular surface microbiome (OSM). The roles of OSM in ocular diseases remain uncertain. In this study, we aimed to determine the composition of OSM and to evaluate its potential roles and functions from multiple cohorts.
View Article and Find Full Text PDFReconstructing the first COVID-19 pandemic wave with minimal data in England.
Epidemics
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Department of Biology, University of Oxford, United Kingdom. Electronic address:
Accurate measurement of exposure to SARS-CoV-2 in the population is crucial for understanding the dynamics of disease transmission and evaluating the impacts of interventions. However, it was particularly challenging to achieve this in the early phase of a pandemic because of the sparsity of epidemiological data. We previously developed an early pandemic diagnostic tool that linked minimum datasets: seroprevalence, mortality and infection testing data to estimate the true exposure in different regions of England and found levels of SARS-CoV-2 population exposure to be considerably higher than suggested by seroprevalence surveys.
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