Flywheel.io: Biomedical Imaging Research Platform

@Flywheel_io

Flywheel accelerates new scientific discoveries in life-science, biotech, and academic imaging research through cloud-scale collaboration .

Vrijeme pridruživanja: svibanj 2015.

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  3. Our CEO, Travis Richardson, presented to the researchers at today...thank you to all attendees.

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  5. We're featured in ! Read about the latest in our data platform for academic, clinical, and life sciences researchers. A grateful nod to and Tommy Vaughan of for contributing to the story

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  7. Read our integration story at : "By integrating Flywheel with the massive computational resources provided by the Penn HPC, run by , we can run computationally demanding jobs at scale across large samples" -

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  8. We are working with many NIH institutes and centers to see how standard can be used for research purposes. See the ways NIH is supporting the use of FHIR at .

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  9. 16. sij

    Imaging has the potential to facilitate communication between patients and radiologists and help patients better understand their radiology reports:

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  10. Read about two new QuickNAT and Nobrainer gears, GPU instances, Federated Training, and a gear wrapping of a past MICCAI BraTS winner.

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  11. 10. sij

    A cardboard ? pioneer Sir Peter Mansfield made his vision vivid in other people's mind with a simple cardboard model (and several years of cutting edge research).

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  12. To accommodate our growing team, we are moving to a larger office space in Minneapolis. We are taking your suggestions for the name of one of our new conference rooms! We have chosen moon names as our theme. Please feel free to suggest your favorite moon🌔

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  13. Our team will be in San Francisco for J.P. Morgan's Annual Healthcare conference next week. If you are interested in meeting our team, drop us a message at info@flywheel.io.

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  14. 8. sij

    An Intelligent Future for Medical Imaging: A Market Outlook on Artificial Intelligence for Medical Imaging

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  15. A great city for a great conference! We look forward to seeing everyone in for .

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  16. 7. sij

    AI imaging research—a guide: 1 define training, validation, test sets 2 test w/ external set 3 use multivendor images 4 justify data volume 5 apply field's reference standard 6 disclose image prep 7 benchmark AI against expert radiologists 8 explain AI decisions 9 share AI source

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  17. 6. sij

    New viewpoint worth reading about reproducible machine learning by . Reproducing machine learning models can be expensive (>1M$ just to rerun the algorithms) but vital before implemented in clinical practice

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  18. Great step towards standardization in / research: A list of requirements by the editorial board.

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  20. 1. sij

    This is now open!! Happy new year everyone!

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