eigenham t1_iseq5zz wrote on October 15, 2022 at 12:26 PM

Medical imaging community doesn't like when you make up new data (which makes sense when you think about the use case). That said, sure there's work on interpolation, but probably a lot of what you're looking for is hiding in the literature as "super resolution imaging". There's a bunch of hand wavy work and a few groups doing really good validation studies (just look for the authors from the biggest and most famous institutions, because the sad truth is you need money and resources to properly validate).

tdgros t1_isey85z wrote on October 15, 2022 at 1:40 PM

https://arxiv.org/pdf/2209.07162.pdf This recent paper released a dataset of 100k brain MRI images generated with a diffusion model. So things are moving a bit...

eigenham t1_isfkldi wrote on October 15, 2022 at 4:29 PM

Yeah that's definitely one of the bigger groups/institutions in this field. You can expect groups like theirs to push the bounds early, and only some of those efforts gain enough traction for actual acceptance in clinical research/practice, so while this is a good effort, the real sign of movement will be when these methods start showing up in clinical journals

Delacroid OP t1_iseuqrb wrote on October 15, 2022 at 1:10 PM

Thank you very much because I didn't know that super resolution also dealt with interpolating. I thought it was only for improving quality of image as going from 360p to 720p. I will try to use the term in my search and see what I get.

freezelikeastatue t1_iseurai wrote on October 15, 2022 at 1:10 PM

Excellent summation! However, you do draw an interesting point, don’t use it to make up data, use it to predict growth, especially for tumors in the brain. I’m not sure you have the proper data sets to feed a model to predict biological growth, but I think that would be an application for your specific use case provided above.