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Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2

arXiv 2111.06693 ↗First posted 12 Nov 2021q-bio.QM cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 56 references that are in the map. First posted on arXiv 12 Nov 2021.

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References · 23

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11 more
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