instantdl 1.0.5

An easy and convenient Deep Learning pipeline for image segmentation and classification

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

[](https://travis-ci.com/marrlab/InstantDL)

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](https://doi.org/10.1101/2020.06.22.164103)

<p align=”center”> <img src=”docs/Instand_DL_farbig_RGB.png” width=”400” /> </p>

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## Documentation

For documentation please refere to [docs](docs)

For a short video introducing InstantDL please see:

<a href=”http://www.youtube.com/watch?v=Wy4wlEyE2fA”> <p align=”center”> <img href=”InstantDL” src=”http://img.youtube.com/vi/Wy4wlEyE2fA/0.jpg” width=”500” align=”center”> </p> <a>

## 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, please cite this paper:

` @article { 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}, year = {2021}, doi = {10.1186/s12859-021-04037-3}, URL = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04037-3#article-info}, eprint = {https://doi.org/10.1186/s12859-021-04037-3}, journal = {BMC Bioinformatics} } `