Single-cell classification based on label-free high-resolution optical data of cell adhesion kinetics
This is the supplementary repository for our paper titled "Single-cell classification based on label-free high-resolution optical data of cell adhesion kinetics". This was originally forked from the DL-4-TSC repository for their time series classification paper titled "Deep learning for time series classification: a review". The codebase was modified to accomodate our dataset for training and valiadtion. Additionally, it contains our evaluation codebase which can be used to generate the results discussed in our paper.
The single-cell time-series dataset and the paper results are available here. The results contain the trained model parameters and metric valies for every model type and cross validation iteration.
All python packages needed are listed in pip-requirements.txt file and can be installed simply using the pip command.
The code now uses Tensorflow 2.0.
The results in the paper were generated using the Tensorflow 1.14 implementation which can be found here.
Using Tensorflow 2.0 should give the same results.
Now InceptionTime is included in the mix, feel free to send a pull request to add another classifier.
If you re-use our work or the dataset, please cite:
and also cite the original DL-4-TSC paper if you use the time-series classification codebase.