Submitted by Delacroid t3_y4kvq2 in MachineLearning
tdgros t1_isez24z wrote
Filling in a missing slice could be called an "inpainting problem".
There is this line of work that should fit your description: https://arxiv.org/pdf/2202.04200.pdf (there are older similar approaches as well). There are approaches using GANs as well. I can't say if they're popular for medical imaging data, but they're quite general.
Red-Portal t1_isg2apg wrote
Interpolation is quite different from inpainting. Inpainting is about filling out information that is outright missing, but super-resolution and interpolation is about filling out only the missing "high-frequency information."
tdgros t1_isg3vwi wrote
You are welcome to call it what you want, I'm pretty sure you see the similarities and why I suggested maskGIT.
Red-Portal t1_isg4hjv wrote
No I don't? Because the usual methods used for frame interpolation or super-resolution are not only quite well established but completely different.
tdgros t1_isg5iy8 wrote
In OP's setting, imho you can use the term you want: inpainting because it's a large missing area, SR because some people see SR as filling in new rows and columns (I don't, I prefer to see it as inverting the lens degradation) and interpolation because it just means "adding things between other things", at least in my native language. I'm not sure what usual methods you are referring to, but you could suggest them to OP!
Delacroid OP t1_iseztbz wrote
Thanks, I never thought about the analogy with an impainting problem.
Edit: grammar
tdgros t1_isezwr3 wrote
this is a weird autocorrection, right? :)
deep-yearning t1_isfpzaa wrote
No, the problem is impairing them
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