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imo the real value of ML in imaging is going to come from the use of variational inference in scope development. due to their raster scanned nature, off the she

imo the real value of ML in imaging is going to come from the use of variational inference in scope development. due to their raster scanned nature, off the shelf 2p scopes can deliver high resolutions or high framerates, but not both. 2p scopes using compressive sensing have been developed to get high resolution at high framerates have been developed, but there is a lot more room for optimization, and the scope i'm thinking of has convergence issues (compressive sensing generally has good problems but this one ends up being ill-posed). 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.

andthenwhat 4y ago
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)
- andbberger 4y ago
  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.

andthenwhat 4y ago

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)

andbberger 4y ago
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.