Dear image.sc community,
we finally released YAPiC (yet another pixel classifier, based on deep learning).
Pixel classification in YAPiC is based on deep learning with fully convolutional neural networks.
YAPiC was designed to make this kind of AI powered pixel classification simply applicable, i.e feasible to use for a PhD student in his/her imaging project.
“Simply applicable” means here in detail:
- Easy to install.
- Working out of the box with 3D multichannel images saved with Fiji.
- Easy collection of label data by utilizing the great Ilastik user interface.
- Support of sparse labels. From our experience, labels can be collected within a few hours by one single person.
- Simple command line and programming interface (Python).
YAPiC was developed by my former co-worker Manuel Schölling and me, as part of our work at the Image and Data Analysis Facility, Core Research Facilities, DZNE Bonn. Within the last years, we applied YAPiC to many imaging projects of our users. It’s now a central tool for us and used on a daily basis.
We are happy to share this useful tool with the community. Of course, we highly appreciate bug reports and pull requests.