[donovanr]

1. All of the above. The label free tool in particular gives you such a big free lunch at the microscope that the combination of it and good visualization has the potential to massively impact research workflows.

2. We are very interested in how cells change as they divide, differentiate, age, are perturbed by their environment, etc. We study cells in culture right now -- getting good images of in vitro cells from multicellular organisms is way harder. So yes it would absolutely be useful. I don't know if we're going to tackle it ourselves, but one of our core missions is to lay the groundwork for the community to take our tools and run with them -- it's a big win for us if we can bring previously unfeasible research within the realm of the possible.

3. I am a Bayesian at heart, so modeling uncertainty is something that I'm always thinking about. It's high on my list of priorities to do something along these lines.

4. Image similarity is a hard problem. At the end of the day, metrics only get you so far and the proof is in the pudding. Unfortunately there is no ground-truth data to test against -- the probabilistic model was constructed exactly because we can't measure where everything is all at the same time. Some things we do to convince ourselves that we're on track is to see that the variation in the imputed predictions and the actual data are statistically similar, and to see if experts are confounded in differentiating the outputs of our models from actual data. You can read more here https://www.biorxiv.org/content/early/2017/12/21/238378