#IMAGEJ LINUX SOFTWARE#
Since most DL methods are available as source code, running them requires setting up sophisticated software and hardware environment. Unfortunately, their accessibility is often riddled with technical challenges for the non-expert user. Beyond its direct use, we expect deepImageJ to contribute to the broader dissemination and reuse of DL models in life-sciences applications and bioimage informatics.ĭeep learning (DL) models have a profound impact on a wide range of imaging applications, including life-sciences. Very recently, several train ing frameworks have adopted the deepImageJ format to deploy their work in one of the most used software in the field (ImageJ). DeepImageJ is compatible with existing state-of-the-art solutions and it is equipped with utility tools for developers to include new models. Hence, non-experts can easily perform common image processing tasks in life-science research with DL-based tools including pixel and object classification, instance segmentation, denoising or virtual staining. The deepImageJ environment gives access to the largest bioimage repository of pre-trained DL models (BioImage Model Zoo).
DeepImageJ is a user-friendly solution that enables the generic use of pre-trained deep learn ing (DL) models for biomedical image analysis in ImageJ.
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