This is a followup to: #7815.

I checked the possibilities to visualize the nifti (and potentially dicom), and here's what I found:

• https://github.com/aces/brainbrowser, AGPL3 license, last commit 3 months ago, latest (github) release from 2017. It's available on jsdelivr: https://www.jsdelivr.com/package/npm/brainbrowser (but that is from 2015!)
• https://github.com/rii-mango/Papaya, custom but BSD-style license that would require datasets to list the conditions in their readme somewhere, last commit June 2024. I looked into this library and it looks mature and good enough for our use case, but just working on it for a short time I wasn't able to get this to work, but am sure we could get this working, would probably require some JS on datasets' end. Available on jsdelivr as well: https://www.jsdelivr.com/package/npm/papaya-viewer. Seems like it's frequently loaded.
• https://github.com/hanayik/niivue, BSD3 license, last commit May 26, 2021. Archived. Doesn't look like an option.

I think the only real option for us Papaya, but there is also the risk that we'll end up with an unmaintained package after a while, since development seems to be slow or even halted.

I think conceptually we would need to figure out how we can build a good solution for visualizing Medical Image data. On shap, we have a separate javascript folder in which we render visualizations, this could be a blueprint but will require a bundler, etc. Alternatively one could go with a naive approach to just write some html code in a python string and load the package via jsdelivr.

@lhoestq thoughts?