Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics

Cancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk

A Slicer extension to provide a GUI around pyradiomics

Hand-crafted radiomics and deep learning-based radiomcis features extraction.

Lesion and prostate masks for the PROSTATEx training dataset, after a lesion-by-lesion quality check.

(Latest semester at https://github.com/kmader/Quantitative-Big-Imaging-2019) The material for the Quantitative Big Imaging course at ETHZ for the Spring Semester 2018

Radiomics (here mainly means hand-crafted based radiomics) contains data acquire, ROI segmentation, feature extraction, feature selection, machine learning modeling, and stastical analysis.

Easylearn is designed for machine learning mainly in resting-state fMRI, radiomics and other fields (such as EEG). Easylearn is built on top of scikit-learn, pytorch and other packages. Easylearn can assist doctors and researchers who have limited coding experience to easily realize machine learning, e.g., (MR/CT/PET/EEG)imaging-marker- or other…

Medical Image Radiomics Processor

The easiest tool for experimenting with radiomics features.

Import, visualize, and extract image features from CT and RT Dose DICOM files in MATLAB.

Clinically-Interpretable Radiomics [MICCAI'22, CMPB'21]

Image processing tools for radiomics analysis

Open source of Pyradiomics extension

Python Implementation of the CoLlAGe radiomics descriptor. CoLlAGe captures subtle anisotropic differences in disease pathologies by measuring entropy of co-occurrences of voxel-level gradient orientations on imaging computed within a local neighborhood.

TriDFusion (3DF) Medical Imaging Viewer

Radiomics Analysis for Prediction of EGFR Mutations and Ki-67 Proliferation Index in Patients with Non-Small Cell Lung Cancer

Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-parametric MRI based Convolutional Neural Network Model

A tool to perform comprehensive analysis of high-dimensional radiomic datasets