An easy and convenient Deep Learning pipeline for image segmentation and classification
Project description
# InstandDL: An easy and convenient deep learning pipeline for image segmentation and classification
[![Build Status](https://travis-ci.com/marrlab/InstantDL.svg?branch=develop-test)](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>
## 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 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.
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