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Interesting. Would love to see an example of a tumor so small a radiologist could see it but that a ML algorithm would smooth out
by ryan93 5y ago
Interesting. Would love to see an example of a tumor so small a radiologist could see it but that a ML algorithm would smooth out
- 01acheru 5y agoIt is not about big or small, I don’t think you understand how ML work. And by the way, tumors can be really small.
- exdsq 5y agoOut of interest, do you understand how MRI reconstruction works?
- 01acheru 5y agoIf your question is literally “you understand how it works?” the answer is yes, I do. If your question is more nuanced to mean “do you really really know how it works, meaning you could work on it tomorrow?” the answer is no, it is not my field.
- ryan93 5y agoNot obvious the human chosen function for reconstruction is necessarily better than the ML one. The human function doesn't save all the data either.
- acerjio 5y agoA lot of the problem comes from the use of generative neural networks. If the prior is that the reconstructed images should "look" a certain way, then the algorithm will favor that. Some of our colleagues did early work with DL and got scared off of generative models due to finding issues with nonphysical results (read: broken layers of cortex in the brain, completely non-physical anatomy) that these models can generate from the undersampled raw data. That said, there are other great ways to incorporate DL into MRI other than recon. I'm more interested in the use of DL for image segmentation, feature detection, potentially denoising, or other techniques on the image processing side. Those make a lot more sense as "top down" tasks that are well suited for neural networks.