Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
6 authors.
Jie YangDepartment of Biomedical Engineering, Columbia University, NY, USA.
Thomas VetterliDepartment of Biomedical Engineering, Columbia University, NY, USA.
Pallavi P BalteDepartment of Medicine, Columbia University Medical Center, New York, NY, USA.
R Graham BarrDepartment of Medicine, Columbia University Medical Center, New York, NY, USA.
Andrew F LaineDepartment of Biomedical Engineering, Columbia University, NY, USA.
Elsa D AngeliniDepartment of Biomedical Engineering, Columbia University, NY, USA.
Funding
Institute for Clinical and Translational Research (UL1)UL1RR025005 · NCRR · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2007 to 2011
$75.8M
Institute for Clinical and Translational ResearchUL1TR001079 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2013 to 2017
$60.1M
CTSA INFRASTRUCTURE FOR PEDIATRIC RESEARCHUL1RR024156 · NCRR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2006 to 2011
$53.0M
Wake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3M
Clinical and Translational Science AwardUL1TR000040 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2012 to 2015
$26.2M
Pulmonary microvascular perfusion in the Multi-Ethnic Study of AtherosclerosisR01HL077612 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI R Graham BARR · 2004 to 2026
$18.8M
Pulmonary Vascular Changes in Early Chronic Obstructive Pulmonary Disease (COPD)R01HL093081 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI R Graham BARR · 2008 to 2026
$17.2M
Chronic obstructive pulmonary disease in non-smokersR01HL130506 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HERBSTMAN, JULIE BETH, SMITH, BENJAMIN M. · 2016 to 2025
$6.3M
Novel Quantitative Emphysema Subtypes in MESA and SPIROMICSR01HL121270 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BARR, R GRAHAM, LAINE, ANDREW FRANCIS · 2014 to 2022
$5.6M
Airway Tree Subtyping on Large Cohorts of CT Images for COPD RiskR01HL155816 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI LAINE, ANDREW FRANCIS, SMITH, BENJAMIN M. · 2021 to 2024
$2.4M
Genome-wide and Linkage Study of Quantitative Emphysema PhenotypesRC1HL100543 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BARR, R GRAHAM, RICH, STEPHEN S. · 2009 to 2010
$970k
SUBCLINICAL CARDIOVASCULAR DISEASE STUDYN01HC095166 · HC · UNIVERSITY OF VERMONT &ST AGRIC COLLEGE · PI TRACY, RUSSELL P · 1999 to 2001
Emphysema quantification and sub-typing is actively studied on cohorts of full-lung high-resolution CT (HRCT) scans, with promising results. Transfer of quantification and classification tools to cardiac CT scans, which involve 70% of the lungs, is challenging due to lower image resolution and degradation of textural patterns. In this study, we propose an original deep-learning domain-adaptation framework to use a pre-existing dictionary of lung texture patterns (LTP), learned on gold-standard full-lung HRCT scans, to label emphysema regions on cardiac CT scans. The method exploits convolutional neural networks (CNNs) trained for: 1) supervised lung texture classification on
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
UNSUPERVISED DOMAIN ADAPTION WITH ADVERSARIAL LEARNING (UDAA) FOR EMPHYSEMA SUBTYPING ON CARDIAC CT SCANS: THE MESA STUDY. · full record | OpenQuestion