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I never understood these arguments. Isn't it like claiming self-driving cars via neural networks would never be possible because you can't test that the neural

I never understood these arguments. Isn't it like claiming self-driving cars via neural networks would never be possible because you can't test that the neural network would take the correct decision in every situation? I view the whole issue stochastically, with the immediate aim being to make (significantly) fewer errors than the current approach, which is having a human decide. I don't claim that I can design an experiment which could selve as an indication whether we are improving upon human judgments, but I think this should be the goal. Reflecting upon my view, i think it comes from the experience of training ml-algorithms. You are always minimizing errors, but you goal is almost never to make 0 errors, because often your data is noisy and you are probably overfitting. I know the medical enviroments are more sensitive, but I can't really wrap my head around how we could design a learning algorithms that does not make any error and works on all abnormalies. I think it will always missclassify. Rephrasing my argument: I think the approval should be given if an significant expected improvement over the distribution of real-life abnormalies can be detected and not over the uniform-dsitribution over all abnormalies. EDIT: detecting out-of-distribution samples is hard and I don't think this is a solution and leads to a false sense of security.

PavlikPaja 8y ago
It isn't. It obviously is possible to drive a car. The problem here is you're trying to reconstruct image from less data than what is fundamentally possible. Compressed sensing is already as low as you can get, unless you prove it is possible to reconstruct the image accurately from even less data, then it's probably pointless to try use neural networks to decode it, and it's actually a bad idea anyway. The problem with neural networks is that they can reconstruct something that looks "normal", not necessarily something that is accurate. The more abnormal the scan, the more likely it will get reconstructed as something that looks perfectly fine even when it's not.
- LeanderK 8y ago
  I really don't know anything about MRI and their usage, i was mainly criticising that his arguments sounded like a lot of the arguments I've heard against other usages of ML-Algorithms. What I meant: There's probably a medical reason why you want such a product and if a more readily available MRI saves (really significantly) more lives than the chance that it might miss some abnormalies which could lead to death, then I think we should allow it. That's what I meant with a stochastic view. If we, for example, only have a few scans per hospital available because the chance might exist that we missclassify something and lots of people get worse or delayed treatment because they are not high enough on the priority list to get access to a super-resulution MRI with a fidelity they don't really need (again, i don't know anything, just to illustrate my point), then I think something is wrong. His argument just sounded dismissive without giving a, to my uninformed point of view, valid reason.
dontreact 8y ago
the distribution of abnormalities should be weighted by the severity of a miss. Unfortunately there are many abnormalities with lethal outcomes if missed. So it’s very hard to get a sense if a reconstruction algorithm will adversely affect outcomes. I would appreciate even a more detailed look at trying to estimate this, especially in light of how much more a deep learning system can be. However that is not what’s happening. Everyone is happy to just jump over the bar that has been set for previous reconstruction algorithms.

PavlikPaja 8y ago

It isn't. It obviously is possible to drive a car. The problem here is you're trying to reconstruct image from less data than what is fundamentally possible. Compressed sensing is already as low as you can get, unless you prove it is possible to reconstruct the image accurately from even less data, then it's probably pointless to try use neural networks to decode it, and it's actually a bad idea anyway. The problem with neural networks is that they can reconstruct something that looks "normal", not necessarily something that is accurate. The more abnormal the scan, the more likely it will get reconstructed as something that looks perfectly fine even when it's not.

LeanderK 8y ago
I really don't know anything about MRI and their usage, i was mainly criticising that his arguments sounded like a lot of the arguments I've heard against other usages of ML-Algorithms. What I meant: There's probably a medical reason why you want such a product and if a more readily available MRI saves (really significantly) more lives than the chance that it might miss some abnormalies which could lead to death, then I think we should allow it. That's what I meant with a stochastic view. If we, for example, only have a few scans per hospital available because the chance might exist that we missclassify something and lots of people get worse or delayed treatment because they are not high enough on the priority list to get access to a super-resulution MRI with a fidelity they don't really need (again, i don't know anything, just to illustrate my point), then I think something is wrong. His argument just sounded dismissive without giving a, to my uninformed point of view, valid reason.

dontreact 8y ago

the distribution of abnormalities should be weighted by the severity of a miss. Unfortunately there are many abnormalities with lethal outcomes if missed. So it’s very hard to get a sense if a reconstruction algorithm will adversely affect outcomes. I would appreciate even a more detailed look at trying to estimate this, especially in light of how much more a deep learning system can be. However that is not what’s happening. Everyone is happy to just jump over the bar that has been set for previous reconstruction algorithms.