• Facebook Aims To Make MRI Scans 10x Faster With NYU
    https://www.forbes.com/sites/samshead/2018/08/20/facebook-aims-to-make-mri-scans-10x-faster-with-nyu/#2b6219047a04

    Si même l’Université de New York a besoin de Facebook pour faire des recherches... mais tout est clean hein, peut être même open source.

    Zitnick added that partnering with NYU could help the social media giant get the technology into practice if it proves to be successful. “If we do show success, we have an avenue to get this out into clinical practice, test it out, put it in front of real radiologists, and make sure that what we’re doing is actually going to be impactful,” he said.

    But when asked if Facebook plans to release and build medical products in the future, Zitnick didn’t give much away. Instead, he said that “FAIR’s mission is to push the science of AI forward,” before going on to say that FAIR is looking for problems where AI can have a positive impact on the world.

    Facebook and NYU have a long-standing relationship, with several people working for both organizations including Yann LeCun, who was the director of FAIR before he became Facebook’s chief AI scientist. “This all got started with a connection by someone working both for NYU and in collaboration with FAIR. They suggested it’d be good for us to start talking, which we did,” said Sodickson.

    Facebook and NYU plan to open source their work so that other researchers can build on their developments. As the project unfolds, Facebook said it will publish AI models, baselines, and evaluation metrics associated with the research, while NYU will open source the image dataset.

    Facebook isn’t the only tech company exploring how AI can be used to assist radiologists. For example, DeepMind, an AI lab owned by Google, has developed deep learning software that can detect over 50 eye diseases from scans.

    DeepMind has a number of other healthcare projects but Facebook (who was reportedly interested in buying DeepMind at one stage) claims this project is the first of its kind, as it aims to change the way medical images are created in the first place, as opposed to using existing medical images to see what can be achieved.

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