Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes

This is the implementation of UCVME for the paper "Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes".

Data

We are working to make the dataset available publicly. We provide reference implementation code for the moment.

Training and testing

To generate global and local radiomic features, run:

python3 gen_radiomic_global.py
python3 gen_radiomic_local_stp1.py
python3 gen_radiomic_local_stp2.py

To perform feature selection, run :

python3 pyft_LASSO_A.py --alpha_param=0.12

To generate a pre-trained DNN model for use, run:

bash script_pretrain.sh

To perform training for RIDL, run:

bash script_ridl.sh

To perform training for hybrid baseline, run:

bash script_hybrid.sh

Notes

• Contact: DAI Weihang (wdai03@gmail.com)

Citation

If this code is useful for your research, please consider citing:

(to be released)