CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies

2021 ◽  
Author(s):  
Prakash KN Bhanu ◽  
Channarayapatna Srinivas Arvind ◽  
Ling Yun Yeow ◽  
Wen Xiang Chen ◽  
Wee Shiong Lim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cohort Studies ◽  
Abdominal Fat ◽  
Large Cohort ◽  
Analysis Tool

scholarly journals Correction to: CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies

2021 ◽  
Author(s):  
K. N. Bhanu Prakash ◽  
Channarayapatna Srinivas Arvind ◽  
Ling Yun Yeow ◽  
Wen Xiang Chen ◽  
Wee Shiong Lim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cohort Studies ◽  
Abdominal Fat ◽  
Large Cohort ◽  
Analysis Tool

scholarly journals Hyperlipidemia in pregnancy

2011 ◽  
Vol 152 (19) ◽  
pp. 753-757 ◽  
Author(s):  
Tatjána Ábel ◽  
Anna Blázovics ◽  
Márta Kemény ◽  
Gabriella Lengyel
Keyword(s):  
Cardiovascular Disease ◽  
Acute Pancreatitis ◽  
Cohort Studies ◽  
Pregnant Women ◽  
Breast Feeding ◽  
Normal Pregnancy ◽  
Large Cohort ◽  
Physiological Changes ◽  
Teratogenic Effects ◽  
In Pregnancy

Physiological changes in lipoprotein levels occur in normal pregnancy. Women with hyperlipoproteinemia are advised to discontinue statins, fibrates already when they consider pregnancy up to and including breast-feeding the newborn, because of the fear for teratogenic effects. Hypertriglyceridemia in pregnancy can rarely lead to acute pancreatitis. Management of acute pancreatitis in pregnant women is similar to that used in non-pregnant patients. Further large cohort studies are needed to estimate the consequence of supraphysiologic hyperlipoproteinemia or extreme hyperlipoproteinemia in pregnancy on the risk for cardiovascular disease later in life. Orv. Hetil., 2011, 152, 753–757.

1140P Validation of a deep learning model for the classification of lung cancer in a large cohort of biopsied samples

2021 ◽  
Vol 32 ◽  
pp. S926-S927
Author(s):  
G. Toyokawa ◽  
Y. Yamada ◽  
N. Haratake ◽  
Y. Shiraishi ◽  
T. Takenaka ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Learning Model ◽  
Large Cohort ◽  
Deep Learning Model

scholarly journals Comparing Video Activity Classifiers within a Novel Framework

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1545
Author(s):  
Chiman Kwan ◽  
Bence Budavari ◽  
Bulent Ayhan
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
General Framework ◽  
Experimental Results ◽  
Analysis Tool ◽  
Activity Detection ◽  
Rule Based ◽  
Event Classification ◽  
Hybrid Combination ◽  
Rule Based Approach

Video activity classification has many applications. It is challenging because of the diverse characteristics of different events. In this paper, we examined different approaches to event classification within a general framework for video activity detection and classification. In our experiments, we focused on event classification in which we explored a deep learning-based approach, a rule-based approach, and a hybrid combination of the previous two approaches. Experimental results using the well-known Video Image Retrieval and Analysis Tool (VIRAT) database showed that the proposed classification approaches within the framework are promising and more research is needed in this area

scholarly journals The prevalence of metabolic syndrome and metabolically healthy obesity in Europe: a collaborative analysis of ten large cohort studies

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Jana V van Vliet-Ostaptchouk ◽  
Marja-Liisa Nuotio ◽  
Sandra N Slagter ◽  
Dany Doiron ◽  
Krista Fischer ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Cohort Studies ◽  
Large Cohort ◽  
Metabolically Healthy Obesity ◽  
Metabolically Healthy ◽  
Collaborative Analysis

scholarly journals Deep learning-based quantification of abdominal fat on magnetic resonance images

PLoS ONE ◽  
2018 ◽  
Vol 13 (9) ◽  
pp. e0204071 ◽  
Author(s):  
Andrew T. Grainger ◽  
Nicholas J. Tustison ◽  
Kun Qing ◽  
Rene Roy ◽  
Stuart S. Berr ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Abdominal Fat ◽  
Magnetic Resonance Images

Sleep Assessment in Large Cohort Studies with High-Resolution Accelerometers

2016 ◽  
Vol 11 (4) ◽  
pp. 469-488 ◽  
Author(s):  
Melanie Zinkhan ◽  
Jan W. Kantelhardt
Keyword(s):  
High Resolution ◽  
Cohort Studies ◽  
Large Cohort ◽  
Sleep Assessment
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