InstandDL: An easy and convenient deep learning pipeline for image segmentation and classification

InstantDL enables experts and non-experts to use state-of-the art deep learning methods on biomedical image data. InstantDL offers the four most common tasks in medical image processing: Semantic segmentation, instance segmentation, pixel-wise regression and classification. For more in depth discussion on the methods, as well as comparing the results and bechmarks using this package, please refer to our preprint on bioRxiv here

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Documentation

For documentation please refere to docs

For a short video introducing InstantDL please see:

Contributing

We are happy about any contributions. For any suggested changes, please send a pull request to the develop branch.

Citation

If you use InstantDL in a project, you can cite the preprint on bioRxiv

@article {Waibel2020.06.22.164103,
author = {Waibel, Dominik Jens Elias and Shetab Boushehri, Sayedali and Marr, Carsten},
title = {InstantDL - An easy-to-use deep learning pipeline for image segmentation and classification},
elocation-id = {2020.06.22.164103},
year = {2020},
doi = {10.1101/2020.06.22.164103},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2020/06/23/2020.06.22.164103},
eprint = {https://www.biorxiv.org/content/early/2020/06/23/2020.06.22.164103.full.pdf},
journal = {bioRxiv}
}

The main publication will be added soon.