Stanford AIMI

@StanfordAIMI

The Stanford Center for Artificial Intelligence in Medicine & Imaging (AIMI) exists to improve health by developing & disseminating the latest AI methods.

Stanford, CA
aimi.stanford.edu
Vrijeme pridruživanja: siječanj 2018.
33 fotografije ili videozapisa Fotografije i videozapisi

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  1. proslijedio/la je Tweet
    31. sij

    Great article about the use of algorithms in the field of featuring SCI member Matthew Lungren and Director Curtis Langlotz.

    8 proslijeđenih tweetova 19 korisnika označava da im se sviđa
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  2. proslijedio/la je Tweet
    27. sij

    "AI won’t replace radiologists, but radiologists who use AI will replace radiologists who don’t," says Director .

    5 replies 74 proslijeđena tweeta 163 korisnika označavaju da im se sviđa
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  3. 23. sij

    We're super excited to announce the release of our latest shared dataset with >10k expert-labeled echocardiogram videos! Check it out at Special thanks to

    0:07
    1/7 We’d like to share something interesting for machine learning in cardiology. We trained a neural network, EchoNet-Dynamic, to interpret videos to trace the left ventricle and estimate ejection fraction. The results are striking:
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    1 reply 56 proslijeđenih tweetova 136 korisnika označava da im se sviđa
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  4. proslijedio/la je Tweet
    18. pro 2019.

    No, we don't want "robot radiologists"! “ won’t replace radiologists, but radiologists who use AI will replace radiologists who don’t.”—

    1 reply 78 proslijeđenih tweetova 143 korisnika označavaju da im se sviđa
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  5. 16. pro 2019.

    We're hiring highly motivated in to perform cutting-edge research with us ! Apply now!

    14 proslijeđenih tweetova 18 korisnika označava da im se sviđa
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  6. 13. pro 2019.

    Super excited about this! Congrats !

    At , we present our new, soon to be available, public EchoNet-Dynamic dataset of 10k deidentified and labeled echocardiograms. . Made possible by
    5 korisnika označava da im se sviđa
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  7. 11. pro 2019.

    Congrats to the lead authors Maya Varma, Mandy Lu, Rachel Gardner & colleagues on exciting work just published !

    1 reply 11 proslijeđenih tweetova 27 korisnika označava da im se sviđa
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  8. 4. pro 2019.

    One of the most meaningful pursuits of is developing scalable tools that elevate health in the places that need it the most. Honored to be a part of this panel.

    Great panel on and at honored to be included!
    1 reply 6 proslijeđenih tweetova 16 korisnika označava da im se sviđa
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  9. 4. pro 2019.

    Great discussions taking place now at the deep learning in Q&A session with & at the AI Theater!

    2 proslijeđena tweeta 5 korisnika označava da im se sviđa
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  10. 4. pro 2019.

    Honored to share our experience with deep learning in at ! Q&A session with & from OSU to follow at 2:30pm at the AI Theater!

    3 proslijeđena tweeta 20 korisnika označava da im se sviđa
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  11. proslijedio/la je Tweet
    3. pro 2019.

    Check out Curtis Langlotz's () educational course on Stanford University Experience tomorrow at 8:30AM, Room S406B!

    2 proslijeđena tweeta 12 korisnika označava da im se sviđa
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  12. proslijedio/la je Tweet
    2. pro 2019.

    Amazing turnout for the ML Challenge Award Ceremony!! – mjesto: McCormick Place North Building

    13 proslijeđenih tweetova 53 korisnika označavaju da im se sviđa
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  13. proslijedio/la je Tweet
    26. stu 2019.

    Great thread about the incredible recent progress of AI in language processing, much more rapid progress than in computer vision. We need a benchmark data set containing radiology reports.

    1/ A decade ago we weren’t sure neural nets could ever deal with language. Now the latest AI models crush language benchmarks faster than we can come up with language benchmarks. Far from having an AI winter, we are having a second AI spring.
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    1 reply 9 proslijeđenih tweetova 27 korisnika označava da im se sviđa
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  14. proslijedio/la je Tweet
    27. stu 2019.

    Fantastic echocardiology work described in this thread by of including the release of another public data set

    0:07
    1/7 We’d like to share something interesting for machine learning in cardiology. We trained a neural network, EchoNet-Dynamic, to interpret videos to trace the left ventricle and estimate ejection fraction. The results are striking:
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    8 proslijeđenih tweetova 21 korisnik označava da mu se sviđa
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  15. 21. stu 2019.

    Kicking off an exciting collaboration with and colleagues!

    10 proslijeđenih tweetova 56 korisnika označava da im se sviđa
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  16. proslijedio/la je Tweet
    19. stu 2019.

    When an deep learning model backfires: —High accuracy but no performance improvement —Incorrect model predictions strongly biased doctor's diagnosis —Very instructive

    9 replies 103 proslijeđena tweeta 209 korisnika označava da im se sviđa
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  17. 18. stu 2019.

    Excited to share our new paper showing human-machine partnership with enables superior diagnostic accuracy!

    43 proslijeđena tweeta 116 korisnika označava da im se sviđa
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  18. 11. stu 2019.

    Great panel discussion on , data sharing & advancements in image recognition in cancer imaging and beyond- LIVE NOW at

    1 reply 1 proslijeđeni tweet 7 korisnika označava da im se sviđa
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  19. 11. stu 2019.

    LIVE NOW! talks about “the gift from yesterday” at

    3 proslijeđena tweeta 11 korisnika označava da im se sviđa
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  20. 11. stu 2019.

    LIVE NOW! Panel discussion on current challenges and workflow needs for developing AI techniques in image processing for cancer imaging

    2 proslijeđena tweeta 11 korisnika označava da im se sviđa
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