I'm using CP and CPA to differentiate between dead and alive cells. I'm really happy with the way your software works. I'm using classifier in CPA to train the machine to automatically fill the two/three classes I have....but I have a question regarding how is the right way to use CPA classifier or, well, I'n not sure I'm using it in the right way.
I understand that I can train the machine to recognise my cells as dead or alive (and it works very well). At the moment I'm doing so on images coming from every single sample in my experiment. In fact, for staining issues and sample variability, I'm not able to build a general rule to identify dead/alive cells. I think the best possible approach would be to have a general training set to apply to each sample, isn't it? So I'm not sure I'm doing the right way.
Another question is related to the theoretical principles about the way machine learning alorithms are applyed in quantitative microscopy. I know that if you have a dataset that you want to classify you usually divide it into a fraction used to train the machine (usually above 60%) and the remaining part of your data are used to validate the rules the machine used to classify them. Here we are dealing with another approach, because we are presenting few examples relative to the total amount of images or objects we want to recognise. I know I'd rather ask this kind of problem to someone in my lab/institution, but it's actually hard for me to find someone working on this topic...(I'm not in the medical field)
Anyway...Thank you very much for this useful software.