Edward-E-S-Wang/Radiomics-Feature-Screen-Pipeline
A complete radiomics feature selection pipeline for binary classification tasks
This tool helps medical imaging researchers and clinicians pinpoint the most important features in large radiomics datasets for binary classification tasks, like disease detection. It takes your raw radiomics features and patient labels as input and outputs a refined, smaller set of key features ready for building more accurate and stable predictive models. It's designed for anyone working with medical images who needs to simplify complex feature data.
Use this if you have a radiomics dataset with many features and a binary outcome, and you need to reduce the number of features to build a robust and interpretable predictive model.
Not ideal if your dataset is not radiomics-focused, if you have a non-binary classification problem, or if you prefer manual, step-by-step feature engineering rather than an automated pipeline.
Stars
36
Forks
2
Language
Python
License
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Category
Last pushed
Mar 10, 2026
Commits (30d)
0
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