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
For documentation please refere to docs
For a short video introducing InstantDL please see:
We are happy about any contributions. For any suggested changes, please send a pull request to the develop branch.
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}
}