FAE

Feature Analysis Explorer (FAE) can help researchers develop a classification model with comparison among different methods. This project was inspired on the Radiomics, and provides a GUI to help analyze the feature matrix, including feature matrix pre-process, model development, and results visualization.

If you publish any work which uses this package, I will appreciate that you could give the following link (https://github.com/salan668/FAE)

Welcome any issues and PR.

Release

The Windows 64 version was release here https://drive.google.com/open?id=1htts7YsfaxKtN1NeDcNU4iksXfjr_XyK (Alternative link is: https://pan.baidu.com/s/1ha66TajeoT6dA-a4Qdt8fA)

If FAE could help in your research, please refer to

Y. Song, J. Zhang, Y. Zhang, Y. Hou, X. Yan, Y. Wang, M. Zhou, Y. Yao, G. Yang. FeAture Explorer (FAE): A tool for developing and comparing radiomics models. PLoS One. 2020. DOI: https://doi.org/10.1371/journal.pone.0237587

Getting Started

Pre-install

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

- imbalanced-learn=0.6.2
- matplotlib=3.2.0
- numpy=1.18.1
- pandas=1.0.1
- pdfdocument=3.3
- pillow=7.0.0
- PyQt5=5.14.1
- PyQtGraph=0.10.0
- pyradiomics=3.0
- reportlab=3.5.34
- scikit-learn=0.22.2
- scipy=1.4.1
- seaborn=0.10.0
- statsmodels=0.11.1

Installing

Just clone it by typing in:

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

Architecture of Project

• DataContainer
  • DataContainer. The structure to contain the data, which also includes methods like saving/loading.
  • DataSeparate. Including functions to separate data into training part and testing part.
• Feature Analysis
  • DataBalance, Sample the cases to make the binary-labels balance.
  • Normalization. Normalize the data to avoid the scale effect of different features.
  • DimensionReduction. Reduce the feature dimension, including PCA.
  • Classifier. Map the features onto the labels.
  • CrossValidation. Estimate model by using cross-validation on the training data set.
  • FeatureSelector. Select the sub-features from the feature matrix.
  • Pipelines. The class to estimate the model with different feature selected method and classifier.
• 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 double-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.
• Report
  • To Generate the report with PDF format.

Document

TODO

Author

• Yang Song
• Jing Zhang
• Guang Yang
• Chengxiu Zhang

License

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

Acknowledge

• Contributor:
  • Yi-lai Pei
  • jmtaysom
  • Zhiyong Zhao
• Demo data support.
  • Yu-dong Zhang, Xu Yan.