Implementation of our paper SnapEnsemFS: A Snapshot Ensembling-based Deep Feature Selection Model for Colorectal Cancer Histological Analysis published in Scientific Reports, Nature (2023).
To install the required dependencies run the following in command prompt:
pip install -r requirements.txt
Required directory structure:
+-- data
| +-- .
| +-- train
| +-- val
+-- PSO.py
+-- __init__.py
+-- main.py
+-- model.py
Then, run the code using the command prompt as follows:
python main.py --data_directory "data"
Available arguments:
--epochs: Number of epochs of training. Default = 100--learning_rate: Learning Rate. Default = 0.0002--batch_size: Batch Size. Default = 4--momentum: Momentum. Default = 0.9--num_cycles: Number of cycles. Default = 5
If you find our paper useful for your research, consider citing us:
@article{chattopadhyay2023snapensemfs,
title={SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis},
author={Chattopadhyay, Soumitri and Singh, Pawan Kumar and Ijaz, Muhammad Fazal and Kim, SeongKi and Sarkar, Ram},
journal={Scientific Reports},
volume={13},
number={1},
pages={9937},
year={2023},
publisher={Nature Publishing Group UK London}
}