IFC is a repository of codes and experiment results for Research on Interpretable Learning Approaches in Resting-state Functional Connectivity Analysis, using Keras (version 2.2.4) with TensorFlow (version 1.12.0) as backend.
See the following publications for examples of this code in use:
- Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder. Jinlong Hu, Lijie Cao, Tenghui Li, Bin Liao, Shoubin Dong, and Ping Li, Computational and Mathematical Methods in Medicine, Volume 2020, Article ID 1394830, 2020.
model.py is the Python code of FCNN model.
cal_weight.py is the Python code of calculating the weight of features.