PT  - JOURNAL ARTICLE
AU  - Dominik Waibel
AU  - Sayedali Shetab Boushehri
AU  - Carsten Marr
TI  - InstantDL - An easy-to-use deep learning pipeline for image segmentation and classification
AID  - 10.1101/2020.06.22.164103
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.06.22.164103
4099  - http://biorxiv.org/content/early/2020/07/02/2020.06.22.164103.short
4100  - http://biorxiv.org/content/early/2020/07/02/2020.06.22.164103.full
AB  - Motivation Deep learning contributes to uncovering and understanding molecular and cellular processes with highly performant image computing algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate, consistent and fast data processing. However, published algorithms mostly solve only one specific problem and they often require expert skills and a considerable computer science and machine learning background for application.Results We have thus developed a deep learning pipeline called InstantDL for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables experts and non-experts to apply state-of-the-art deep learning algorithms to biomedical image data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows to assess the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible.Availability and Implementation InstantDL is available under the terms of MIT licence. It can be found on GitHub: https://github.com/marrlab/InstantDLContact carsten.marr{at}helmholtz-muenchen.deCompeting Interest StatementThe authors have declared no competing interest.