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Nvidia Researchers Generate Synthetic Brain MRI Images For AI Research (zdnet.com)

Posted by msmash on from the pushing-the-limits dept.

AI holds a great deal of promise for medical professionals who want to get the most out of medical imaging. However, when it comes to studying brain tumors, there's an inherent problem with the data: abnormal brain images are, by definition, uncommon. New research from Nvidia aims to solve that. From a report: A group of researchers from Nvidia, the Mayo Clinic, and the MGH & BWH Center for Clinical Data Science this weekend are presenting a paper on their work using generative adversarial networks (GANs) to create synthetic brain MRI images. GANs are effectively two AI systems that are pitted against each other -- one that creates synthetic results within a category, and one that identifies the fake results. Working against each other, they both improve. GANs could help expand the data sets that doctors and researchers have to work with, especially when it comes to particularly rare brain diseases.

8 of 48 comments (clear)

  1. Link to Paper by Anonymous Coward · · Score: 2, Interesting

    On GAN's generally, since no actual research is linked to, here: https://arxiv.org/pdf/1406.2661.pdf

    1. Re:Link to Paper by ShanghaiBill · · Score: 4, Informative

      On GAN's generally, since no actual research is linked to, here: https://arxiv.org/pdf/1406.266...

      That was a profoundly influential paper. A must read for anyone interested in modern AI.

      Ian Goodfellow, the primary author, also co-authored Deep Learning, the best book available for learning about deep neural nets.

  2. GANs for data augmentation? by tempmpi · · Score: 4, Insightful

    I wonder if this can actually work. If you don't have enough images to train a classifier, why would training a GAN work? And even if training a GAN works, those images won't contain any information about tumors that were not already contained in the original images.

    --
    Jan
    1. Re:GANs for data augmentation? by RandCraw · · Score: 2

      Yes, and a GAN can easily combine features in unnatural ways that never existed in nature.

      Unless the natural probability distribution of each clinically important feature can be accurately modeled by the deep net, combinatoric perversity is all too likely. (Features combos will arise synthetically that do not occur together in nature, but these images won't be excluded due to the lack of counterexamples in the training set).

      I work in medical image analysis professionally, and I'm certain no physician or biologist would approve of faking images to learn about abnormalities / disease, no matter how 'plausible' they look. (However, GANs might be warranted if the objective is only to model the range of natural anatomic variation, rather than unnatural variations.)

      There are better ways to augment pathology image data that avoid fakery.

  3. On the nose by nospam007 · · Score: 3, Insightful

    Artificial Intelligence finds artificial brain damage.

    1. Re:On the nose by PolygamousRanchKid+ · · Score: 2

      Artificial Intelligence finds artificial brain damage.

      This is a synthetic brain, not an artificial brain, Jim.

      So the really interesting question, for the IgNobel, is . . . "Do synthetic brains dream of polyester sheep . . . ?"

      --
      Schroedinger's Brexit: The UK is both in and out of the EU at the same time!
  4. Re:Will just regurgitate what was already known by religionofpeas · · Score: 3, Insightful

    I'm glad that for anything that scientist have thought of, there's always a slashdot expert who knows better.

  5. Re:Will just regurgitate what was already known by religionofpeas · · Score: 2

    I'm very glad that "knowing about neural networks" allows some of you to dismiss the results of senior research scientists who actually did the work. Have some of you actually read the paper ?