RadView Standardized Radiomics Image Analysis Platform
For imaging researchers and data scientists needing analysis‑ready, reproducible datasets
Cloud platform for consistent radiomics feature extraction with transparent parameters and export-ready outputs across patient cohorts.
Radiomics Workflow Overview
From ingestion to exportable datasets
Research-Grade Capabilities
Built for radiomics studies that need consistent, shareable results
Collaborative Cloud Access
Run shared experiments in the browser with parallel processing and centralized study workspaces
Structured Study Management
Organize imaging as Patient → Study → Series with DICOM protocol detection and batch operations
Preprocessing & Radiomics Features
Build visual pipelines across 11 preprocessing modules, then extract 100+ radiomics features across 13 categories with 23 statistical measures
Statistical Feature Selection
Rank informative features with MRMR, RankSum, and T-Test, including cross-validation support
Visualization & ROI Tools
Review axial, coronal, and sagittal views with measurements, mask drawing, and histogram analysis
AI Model Training
Train classification, regression, or survival models on selected radiomics features using algorithms such as Random Forest, SVM, and XGBoost
Complete Analysis Workflow
A streamlined pipeline from image upload to exportable feature sets
Upload
Import DICOM, MHA, or NIfTI data and validate studies for analysis
Prepare
Apply a preprocessing pipeline so images are standardized across the cohort
Extract
Run radiomics extraction with transparent, documented parameter settings
Select
Keep the most informative features for modeling and cohort comparison
Review
Inspect images, ROIs, and summaries before releasing results
Export
Deliver CSV feature tables plus MHA, NIfTI, and PDF outputs for research use
Watch RadView in Action
See RadView Cloud handling real imaging workflows
RadView Cloud - Experiment Management
Coordinate studies with collaborative experiment tracking
Evidence & Documentation
Grounded in peer-reviewed methods with transparent parameters
Peer-Reviewed Foundations
Feature definitions and preprocessing steps align with established radiomics methodology.
Review publicationsTransparent Parameters
Documented settings and export formats make results easy to reproduce and audit.
View documentationRadView FAQs
Answers for research teams evaluating RadView
Which data formats are supported?
RadView supports DICOM, MHA, and NIfTI for input, with exports available in CSV, MHA, NIfTI, and PDF.
Is RadView available on premise?
RadView is currently available as a secure Cloud Platform, ensuring you always have access to the latest processing pipelines.
What outputs are ready for machine learning?
Feature tables export to CSV with consistent naming and statistical measures designed for downstream modeling.
How do I evaluate RadView Cloud?
Open Evaluate RadView on Your Data to run RadView Cloud in the browser and review collaboration and compute workflows for your study.
Ready to See RadView on Your Data?
Open RadView Cloud in this workspace or watch the walkthrough.