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InstantDL - An easy-to-use deep learning pipeline for image segmentation and classification

View ORCID ProfileDominik Waibel, View ORCID ProfileSayedali Shetab Boushehri, View ORCID ProfileCarsten Marr
doi: https://doi.org/10.1101/2020.06.22.164103
Dominik Waibel
1Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
2Technical University of Munich, School of Life Sciences, Weihenstephan, Germany
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Sayedali Shetab Boushehri
1Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
3Roche Innovation Center Munich, Roche Diagnostics GmbH, Penzberg, Germany
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Carsten Marr
1Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
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Abstract

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/InstantDL

Contact carsten.marr{at}helmholtz-muenchen.de

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/marrlab/InstantDL

  • https://hmgubox2.helmholtz-muenchen.de/index.php/s/YXRD4a7qHnCa9x5

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted July 02, 2020.
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InstantDL - An easy-to-use deep learning pipeline for image segmentation and classification
Dominik Waibel, Sayedali Shetab Boushehri, Carsten Marr
bioRxiv 2020.06.22.164103; doi: https://doi.org/10.1101/2020.06.22.164103
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InstantDL - An easy-to-use deep learning pipeline for image segmentation and classification
Dominik Waibel, Sayedali Shetab Boushehri, Carsten Marr
bioRxiv 2020.06.22.164103; doi: https://doi.org/10.1101/2020.06.22.164103

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