#resonance_magnetique

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

https://thumbor.forbes.com/thumbor/600x315/https%3A%2F%2Fblogs-images.forbes.com%2Fthumbnails%2Fblog_5640%2Fpt_5640_276_o.jpg%3Ft%3D1534777204

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.

#Facebook #Résonance_magnétique #Neuromarketing #Intelligence_artificielle #Université #Partenariats

Facebook and NYU School of Medicine launch research collaboration to improve MRI – Facebook Code
▻https://code.fb.com/ai-research/facebook-and-nyu-school-of-medicine-launch-research-collaboration-to-improv

https://code.fb.com/wp-content/uploads/2018/08/mri2.jpg

C’est bô le langage fleuri des experts en public relation...

Using AI, it may be possible to capture less data and therefore scan faster, while preserving or even enhancing the rich information content of magnetic resonance images. The key is to train artificial neural networks to recognize the underlying structure of the images in order to fill in views omitted from the accelerated scan. This approach is similar to how humans process sensory information. When we experience the world, our brains often receive an incomplete picture — as in the case of obscured or dimly lit objects — that we need to turn into actionable information. Early work performed at NYU School of Medicine shows that artificial neural networks can accomplish a similar task, generating high-quality images from far less data than was previously thought to be necessary.

In practice, reconstructing images from partial information poses an exceedingly hard problem. Neural networks must be able to effectively bridge the gaps in scanning data without sacrificing accuracy. A few missing or incorrectly modeled pixels could mean the difference between an all-clear scan and one in which radiologists find a torn ligament or a possible tumor. Conversely, capturing previously inaccessible information in an image can quite literally save lives.

Advancing the AI and medical communities
Unlike other AI-related projects, which use medical images as a starting point and then attempt to derive anatomical or diagnostic information from them (in emulation of human observers), this collaboration focuses on applying the strengths of machine learning to reconstruct the most high-value images in entirely new ways. With the goal of radically changing the way medical images are acquired in the first place, our aim is not simply enhanced data mining with AI, but rather the generation of fundamentally new capabilities for medical visualization to benefit human health.

In the interest of advancing the state of the art in medical imaging as quickly as possible, we plan to open-source this work to allow the wider research community to build on our developments. As the project progresses, Facebook will share the AI models, baselines, and evaluation metrics associated with this research, and NYU School of Medicine will open-source the image data set. This will help ensure the work’s reproducibility and accelerate adoption of resulting methods in clinical practice.

What’s next
Though this project will initially focus on MRI technology, its long-term impact could extend to many other medical imaging applications. For example, the improvements afforded by AI have the potential to revolutionize CT scans as well. Advanced image reconstruction might enable ultra-low-dose CT scans suitable for vulnerable populations, such as pediatric patients. Such improvements would not only help transform the experience and effectiveness of medical imaging, but they’d also help equalize access to an indispensable element of medical care.

We believe the fastMRI project will demonstrate how domain-specific experts from different fields and industries can work together to produce the kind of open research that will make a far-reaching and lasting positive impact in the world.

#Resonance_magnetique #Intelligence_artificielle #Facebook #Neuromarketing

How Facebook — yes, Facebook — might make MRIs faster
▻https://money.cnn.com/2018/08/20/technology/facebook-mri-ai-nyu/index.html

https://i2.cdn.turner.com/money/dam/assets/180820092705-mri-780x439.jpg

Impeccable pour le neuromarketing...

Doctors use MRI — shorthand for magnetic resonance imaging — to get a closer look at organs, tissues and bones without exposing patients to harmful radiation. The image quality makes them especially helpful in spotting soft tissue damage, too. The problem is, tests can take as long as an hour. Anyone with even a hint of claustrophobia can struggle to remain perfectly still in the tube-like machine that long. Tying up a machine for that long also drives up costs by limiting the number of exams a hospital can perform each day.

Computer scientists at Facebook (FB) think they can use machine learning to make things a lot faster. To that end, NYU is providing an anonymous dataset of 10,000 MRI exams, a trove that will include as many as three million images of knees, brains and livers.

Related: What happens when automation comes for highly paid doctors

Researchers will use the data to train an algorithm, using a method called deep learning, to recognize the arrangement of bones, muscles, ligaments, and other things that make up the human body. Building this knowledge into the software that powers an MRI machine will allow the AI to create a portion of the image, saving time.

#Résonance_magnetique #Neuromarketing #Facebook