CDeep3M-Preview: Online segmentation using the deep neural network model zoo [article]

Matthias G Haberl, Willy Wong, Sean Penticoff, Jihyeon Je, Matthew Madany, Adrian Borchardt, Daniela Boassa, Steven T Peltier, Mark H Ellisman
2020 bioRxiv   pre-print
AbstractSharing deep neural networks and testing the performance of trained networks typically involves a major initial commitment towards one algorithm, before knowing how the network will perform on a different dataset. Here we release a free online tool, CDeep3M-Preview, that allows end-users to rapidly test the performance of any of the pre-trained neural network models hosted on the CIL-CDeep3M modelzoo. This feature makes part of a set of complementary strategies we employ to facilitate
more » ... loy to facilitate sharing, increase reproducibility and enable quicker insights into biology. Namely we: (1) provide CDeep3M deep learning image segmentation software through cloud applications (Colab and AWS) and containerized installations (Docker and Singularity) (2) co-hosting trained deep neural networks with the relevant microscopy images and (3) providing a CDeep3M-Preview feature, enabling quick tests of trained networks on user provided test data or any of the publicly hosted large datasets. The CDeep3M-modelzoo and the are open for contributions of both, trained models as well as image datasets by the community and all services are free of charge.
doi:10.1101/2020.03.26.010660 fatcat:enyjjbo6mrhcvipyysdgavi42e