RADIUMA: A Shareable Executable Workflow Generator for Medical Image Analysis
Bibliographic record
Abstract
We present RADIUMA, an open-access, zero-code, and shareable workflow generator for medical image analysis and machine learning model development. RADIUMA comprises multiple tools for 2D/3D image processing and machine learning, and supports a wide range of imaging modalities, including PET, SPECT, CT, MRI, Ultrasound (US), and Xray, with compatibility for various image formats such as DICOM, NIfTI, and NRRD. The integrated image viewer enables multi-planar visualization, and supports interactive segmentation and exporting segmentation in different formats, including RT-Struct. Moreover, RADIUMA includes multiple image registration and fusion algorithms to facilitate multimodal image analysis. It also offers comprehensive image preprocessing options using various filters, and supports advanced radiomics analysis, extracting radiomics features including shapes, intensity, texture, and deep learning features. RADIUMA includes a full suite of machine learning tools, supporting the entire executable and graphical pipeline, from feature preprocessing and selection to the development of tools for classification, regression, and clustering. Each tool can be used independently or integrated into a complete workflow. Users can construct workflows that begin with loading multi-modality DICOM images, and continue through processing, diagnosis, and prognostic modeling. Executable and graphical workflows can be saved, modified, or extended later, and most importantly, shared with other researchers and users. This allows reproducible analysis using the same parameters and settings across datasets. RADIUMA supports the principles of open science by providing a usable, reusable, reproducible, shareable, and executable workflow generator environment for the full pipeline of medical image analysis and machine learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.041 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".