Hi All,
We have become quite interested in autoscoring of images of crystallization droplets (I think everyone goes through this stage, most grow out of it eventually) and as part of our work we wanted to develop a well-scored set of images to use as a training set for machine learning approaches. We decided that we would try to classify into four classes
Clear
Precipitate
Crystal
Other
This very limited scoring would hopefully be more robust than more granular scoring, but would give enough information to both help find crystals and to help find phase boundaries. We have developed a mobile app - CINDER - which uses swipe technology to assign a score to an image. Every swipe (score) is then recorded in a database, and we would use the consensus score for each image as our training set. Four scores also mean that we can assign a direction to each swipe (swiping right for crystal, up for precipitate etc).
To make the app useful to novices, we have included a training set in the CINDER app - this training presents the user with an image, then shows the user how we would have scored it, and gives an explanation of why we scored it the way we did. Currently our training set ("Cinder Kinder") consists of 75 images. We believe that the Cinder Kinder feature might be quite useful to train students how to look at crystallization experiments in general.
If you have students or relatives or colleagues who might be interested in a 'citizen science' project, we would be delighted if you could tell them about CINDER (currently available through Googleplay for android devices, iOS version coming soon). If you think Cinder Kinder might be useful, and want to include some of your images in the training set, then we would be happy to do so - what we would need is the image, and a score (one of the four above) and a reason as to why you would assign that score. Send any images for inclusion to [log in to unmask] Bugs, improvements and comments all welcome!
Cheers, Janet
|