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InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification.
Dominik Jens Elias Waibel1,2, Sayedali Shetab Boushehri1,2,3, Carsten Marr4
1Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
BMC Bioinformatics
|March 3, 2021
Summary
InstantDL is a deep learning pipeline for image analysis, simplifying tasks like segmentation and classification for researchers. This tool offers accessible, state-of-the-art deep learning for biomedical image processing with minimal coding required.
Area of Science:
- Biomedical image analysis
- Computational biology
- Machine learning applications
Background:
- Deep learning algorithms, particularly convolutional neural networks, excel at molecular and cellular image processing.
- Existing algorithms often address single problems and demand significant coding expertise and machine learning knowledge.
Purpose of the Study:
- To develop a user-friendly deep learning pipeline for common biomedical image processing tasks.
- To enable researchers with basic computational skills to apply advanced deep learning methods.
Main Methods:
- Developed InstantDL, a deep learning pipeline for semantic segmentation, instance segmentation, pixel-wise regression, and classification.
- Automated and standardized workflows for robustness and tested across diverse scenarios.
- Integrated uncertainty assessment for predictions.
Main Results:
- InstantDL successfully processes four common image analysis tasks with minimal user effort.
- Benchmarked on seven public datasets, achieving competitive performance without parameter tuning.
- Code is accessible and documented for customization.
Conclusions:
- InstantDL empowers biomedical researchers with an easy-to-use pipeline for reproducible image processing.
- Facilitates the application of state-of-the-art deep learning in biological research.
