Code for the paper "Flow cytometry with event-based vision and spiking neuromorphic hardware" at CVPR 2023 Workshop on Event-based Vision (workshop link, conference link).
For reference, the code for the paper "Training a spiking neural network on an event-based label-free flow cytometry dataset" at NICE 2023 (link) is provided in the NICE23 folder.
Download and unpack the data from DOI:10.5281/zenodo.7804830. Place it into the data folder.
Use metavision to read the .raw files, then convert them to numpy files. For less disk space usage, use expelliarmus to save the data into .raw files again (as of early 2023, expelliarmus did not read the original raw files correctly - therefore make sure to read them with metavision first). See also script_raw_to_numpy_data.py.
Install the required libraries with conda with the provided .yml file.
conda env create -f environment.ymlProcess the data using script_compress_data.py (downsampling and low-pass-filtering). With the default parameters, this should result in a ~10x compression, but the script will take quite some time to run. There is multiprocessing support so you can run the script using multiple cores, simply change N_PROCESSORS. Keep in mind that each process will require 4GB to 10GB of memory (with 32GB of memory, I was able to run the script using N_PROCESSORS = 3 in ~1h40min).
Some scripts and notebooks to plot the data are provided in the plots folder.
The following files train models on frame representations of the data:
- train_frames_bin_lin.ipynb: linear model
- train_frames_bin_fnn.py: MLP
- train_frames_bin_cnn.py: CNN See the files for performance results.
The training script is train_slayer.py. It uses the dataset as defined in bcdataset.py and the network as defined in bdnetwork.py.
The notebook loihi_preparation.ipynb loads the trained SNN and converts it to a format that is understandable by Loihi. The notebook loihi_inference.ipynb is then used to run the SNN on Loihi.
The results of this study can be found in the results folder.