NeuriteNet is a machine learning platform developed to identify differences in control and experimental groups of cultured neurons based on morphological criteria
This repository has the python code for training and running the NeuriteNet model as well as additional tools for deep learning interpretability. For more details, please check the related publication in the Journal of Neuroscience Methods:
Since the initial publication, our continued work with this project has expanded the capabilities of NeuriteNet and furthered our understanding of the underlying mechanisms of our trained models. As part of this, we have added python code for updated training methods as well as for computing the Bidirectional Relevance (Br) scores to quantitatively evaluate the influence of different measured concepts on the classification outputs of the model. For more details, please check out the upcoming publication.
If you would like to replicate our results or try approaches of your own, the data we used for this project is available at:
- TensorFlow 2.0+
- tqdm
- NumPy
- OpenCV
- Scikit-Image
- tqdm
- NumPy
- Pandas
- Scikit-Learn
- Scipy
- tqdm