Workflow for Optimal Radiomics Classification

Ontology-guided Radiomics Analysis Workflow (O-RAW)

Ontology-guided Radiomics Analysis Workflow (O-RAW)

Workflow for Radiomics and Dosiomics Feature Extraction

radiomics workflow

A consensus radiomics workflow definition (proposal) initiated bei the SPP 2177 Radiomics

Ontology-guided Radiomics Analysis Workflow (O-RAW)

A workflow for CT-Radiomics using (traditional) machine learning method

Code and documentation for my radiomics TCC, focusing on reproducibility, feature-selection stability, and penalized models (LASSO/Elastic Net). Includes data processing, cross-validation pipelines, stability metrics, and reproducible workflows.

NEXUS — A browser-based AI radiomics pipeline GUI built with Flask. Supports end-to-end workflows: texture feature extraction (FOF, GLCM, GLRLM, GLSZM), ML classification, CNN training via GPU subprocess, and single-image prediction.

The purpose of this project is to automate the radiomics workflow and find the best composition of features, settings and parameters that can be used in order to achieve the best performance. Performance can be measured with some specific metrics (like accuracy or AUC) after our AI model has been trained.

Workflows for radiomics

This is a whole workflow of radiomics。

General Analysis Workflow for Radiomics Projects using Pyradiomics & R

[ISBI 2026] From Global Radiomics to Parametric Maps: A Unified Workflow Fusing Radiomics and Deep Learning for PDAC Detection

Convert 3D Slicer radiomics CSVs into a sample-by-feature table for easier machine learning and statistical analysis workflows.

MVO-RadAI provides a reproducible workflow for automated microvascular obstruction (MVO) segmentation on cardiac MRI and radiomics-based prognostic modeling.

A complete workflow for predicting pure uric acid urinary stones that includes semi-auto segmentation stones, radiomics feature extraction, and machine learning prediction.

PrediCT GSoC 2026 repository containing two cardiac CT pipelines: a lightweight U-Net for heart segmentation and a radiomics workflow for calcium feature extraction, Agatston scoring, and downstream analysis.

Code and workflows for radiomic feature analysis and model interpretability across photoacoustic and CT imaging, including feature extraction, sensitivity testing, ML classification, and SHAP analysis.

This project contains a series of Python scripts for extracting radiomics features from medical imaging data and performing clustering analysis and evaluation. The scripts cover the entire workflow from feature extraction to clustering metric calculation.

This project contains a series of Python scripts for extracting radiomics features from medical imaging data and performing clustering analysis and evaluation. The scripts cover the entire workflow from feature extraction to clustering metric calculation.

This project contains a series of Python scripts for extracting radiomics features from medical imaging data and performing clustering analysis and evaluation. The scripts cover the entire workflow from feature extraction to clustering metric calculation.

Radiomics-based machine learning pipeline for classifying low-grade and high-grade gliomas from MRI scans. Includes full workflow: preprocessing, feature extraction, feature selection, model training, and evaluation using metrics such as ROC-AUC, accuracy, and confusion matrices. Achieved 90% accuracy with CatBoost.