Re:Deep Learning in Neuroimaging
by andthenwhat on Tuesday May 03, 2022 @08:01AM
(#31247261)
How do you avoid the problem of using data-trained algorithms to provide measurements (in that they can’t)?
The idea of classifying or localizing detections with deep learning (or any data-trained approach) seems totally reasonable, since it’s clearly making an inference, and that should be clear to the human user. Enhancing or gap-filling the measurements with data-trained approaches would turn the measurements themselves into inference, which seems in opposition of the diagnostic goals (algorithm in-painting non-sensed information learned from the training set)
Re:Deep Learning in Neuroimaging
by andbberger on Tuesday May 03, 2022 @06:15PM
(#31254309)
carefully. that's the whole problem. the beauty of compressed sensing is that it comes with a convergence guarantee. figuring out how to provide similar (but probably not as strong guarantees) with more sophisticated models of the prior is what is going to allow this to work.
variational inference is basically super-powered compressive sensing in this context. compressive sensing exploits the correlated nature of voxels in the iamge via some sparsity in some domain, it's a pretty weak prior. there's a lot more structure to be exploited. the statistics of natural tissue can be modeled with ML, as can the physics of the measurement process.
the trick is posing the problem in a way that comes with convergence guarantees. to my knowledge no one has done this yet. a lossy microscope is not very useful.