We present deepflash2, a deep learning solution that facilitates the objective and reliable segmentation of ambiguous bioimages through multi-expert annotations and integrated quality assurance. Thereby, deepflash2 addresses typical challenges that arise during training, evaluation, and application of deep learning models in bioimaging. The tool is embedded in an easy-to-use graphical user interface and offers best-in-class predictive performance for semantic and instance segmentation under economical usage of computational resources.
@article{arxiv.2111.06693,
title = {Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2},
author = {Matthias Griebel and Dennis Segebarth and Nikolai Stein and Nina Schukraft and Philip Tovote and Robert Blum and Christoph M. Flath},
journal= {arXiv preprint arXiv:2111.06693},
year = {2021}
}