arnaoutlab
@arnaoutlab
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San Francisco
Joined January 2018
In machine learning, data is key. Our paper in @CircAHA sets forth principles for sharing data and protecting patient privacy. @UCSF_BCHSI
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When it comes to imaging datasets, ‘more is better’ is untenable. Using ENRICH to curate datasets @arnaoutlab achieved benchmark performance on DL tasks using only a fraction of the available images & w/o up-front data labeling. Published in @JAMIA_Journal
https://t.co/a9P7rRVg1U
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Congratulations to UCSF @rima_arnaout @UCSF_BCHSI on the being a 2023 @aimbe fellow! Election to the AIMBE College of Fellows is among the highest professional distinctions accorded to a medical and biological engineer! https://t.co/9rnFKHKu5v
@UCSFMedicine @UCSFCardiology
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Excellent poster by Dr Aneela Reddy on finding a composite biometric for fetal CHD detection that’s greater than the sum of its parts!
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Terrific AI/ML main session sponsored by the new @AHAScience Data Science & Precision Medicine Committee! #AHA22 @rohan_khera @arnaoutlab @DemiladeMD @jpirruccello @NorrinaA @SvatiShah
.@Rohan_khera kicks off the session “Implementing Machine Learning for Precision Care: Crossing the Bridge to Reality” Join us in Main III now! @AHAScience #AHA22
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R. Arnaout: crossing the bridge to reality means designing models that generalize @UCSFCardiology @UCSF_BCHSI #AHA22
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CZ Biohub and @GladstoneInst have signed an affiliation agreement to increase our collaborations. Read more about this exciting affiliation that will combine resources and skills to help scientists at both institutions accelerate discovery: https://t.co/8yxY0up3HS
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Amazing @NIH workshop on AI and Cardiovascular imaging! Thanks to my wonderful co-chairs and everyone who made it a success from across research, government, and tech! @NHLBI @NIBIBgov @NSF @CMSGov @NVIDIAHealth @GoogleHealth @awscloud @ACCinTouch @American_Heart and others!
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Arnaout lab is hiring! https://t.co/zfVJCVcLiv
jobrxiv.org
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#ASE21 is finally here! Thanks @EricTopol for highlighting the work of @arnaoutlab in your Keynote! @ASE360 @moon_grady @ucsf_echo @ucsfpedsres @UCSFCardiology @CHD_education @CongenitalHear3 @ACHD_CHI
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Heart defects are the most common kind of birth defect, yet are rare enough that detecting them is difficult. @UCSF researchers' work could change that and allow more early interventions to help children. Via @UCSFHospitals #FutureofMedicine
https://t.co/Ienw7cikQW
ucsf.edu
UCSF researchers have found a way to double doctors’ accuracy in detecting the vast majority of complex fetal heart defects in utero.
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4-year countywide study of out-of-hospital cardiac arrest patients examines the factors that predispose to survival after resuscitation. https://t.co/Bov0KXblp3
#JACC #CardioTwitter #MedTwitter
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Also, to postdocs out there who want to use their computational skills for a project with healthcare impact, we are hiring
jobrxiv.org
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