4 ms·

Playing devil's advocate here but can't machine learning be used to remove noise rather than add detail? Removing noise would reveal detail hidden in the data k

• halpert 5y ago
  "Removing noise" is equivalent to adding detail.
• olliej 5y ago
  The problem would be blurring or denoising meaningful information, but I don't know enough to say they don't do any denoising. I can imagine the data being noisy, but perhaps it isn't? shrug :D
  • notarandomer 5y ago
    Yes, accelerating MRI acquisition increases noise in the images as well as introducing aliasing artifacts. I think the issue is that some modern reconstruction methods (e.g. compressed sensing that was mentioned in the article) produce predictable biases, e.g. adding a risk of smoothing out details, but for ML we don't always know in what way it will bias the reconstructed image (add details, remove important information...), and I think that is what people often worry about.
• aix1 5y ago
  Personally, I am not even sure that "ML vs non-ML" is a useful dichotomy. If method A can be rigorously demonstrated[*] to produce superior results to method B, does it even matter whether mathematically it is constructed out of fast Fourier transforms, Metz filters, layers of convolutions or whatever else? [*] For example, by measuring the quality of reconstruction of a known image (e.g. a real or digital phantom) or, in the ideal world, by evaluating clinical outcomes.