FAE

Feature Analysis Explorer (FAE) can help researchers develop a classification model with comparision among diffferent methods. This project was inspired on the Radiomics, and provides some functions to help extract features with batch process.

A demo of features and the corresponding result are shown below

Demo of Features:

Result processed by FAE

If you publish any work which uses this package, I will appreciate that you could cite the following publication: Song Y, Zhang YD, Yan X, Liu H, Zhou M, Hu B, Yang G, Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI. J Magn Reson Imaging. 2018 Apr 16. doi: 10.1002/jmri.26047.

Welcome any issues and PR.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisties

The below modules must be installed first to make the FAE work.

- pyradiomics
- pandas
- numpy
- scikit-learn
- scipy
- matplotlib (seaborn)

Installing

Just clone it by typing in:

git clone https://github.com/salan668/FAE.git

If you would like to use FAE in any project, please add the path in your system envirement. A trick method is to create a .pth file in the site-packages folder (\Lib\site-packages) and add a path that point to the root folder of the FAE.

Running the example.

cd FAE
python Example\example_diff_method.py

Architecutre of Project

• DataContainer
  • Data Container. The structure to contain the data, which also includes methods like saving/loading and data normaliztion processing.
  • DataSeperate. Including functions to seperate data into training part and testing part.
• Feature Analysis
  • Classifier. The classifier to develop the model, including SVM, AE, Random Forests, LDA.
  • CrossValidation. The CV model to estimate the model. Return the metrics
  • FeturePipeline. The class to estimate the model with different feature selected method and classifier.
  • FeatureSelector. The class to select features, which including 1) remove non-number features, e.g. the version of the pyradiomics; 2) remove non-useful features, e.g. the VolumnNum; 3) different method to select features, like ANOVA, PCA, RFE, Relief.
• Image2Feature
  • RadiomicsFeatureExtractor. This class help extract features from image and ROI with batch process. This class should be more "smart" in the future.
• Visulization.
  • DrawDoubleLine. This function helps draw doulbe-y plot. e.g. plot accuracy and error against the number of iterations.
  • DrawROCList. This function helps draw different ROC curves. AUC will be calculated automaticly and labeled on the legend.
  • FeatureRelationship. This function helps draw the distribution of the values of different features. I can only show at most 3 features in one figure.
  • FeatureSort. This function helps draw the features and the weights of them on the classifiction model.
  • PlotMetricVsFeatureNumber. This function helps draw the AUC / Accuracy / other metrics against the number of chosen features. This can help find the adaptive number of the features.

Document

TODO

Author

• Yang Song

License

This project is licensed under the GPL 3.0 License - see the LICENSE.md file for details

Acknowledge

• Contributor:
  • Guang Yang
  • Yi-lai Pei
  • jmtaysom
• Bugs fix:
  • Jing Zhang.
• Demo data support.
  • Yu-dong Zhang, Xu Yan.