# An automated machine learning framework to optimize radiomics model construction validated on twelve clinical applications

arXiv: https://arxiv.org/abs/2108.08618 · HTML: https://mlsanity.com/paper/2108.08618/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 100 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Image biomarker standardisation initiative](https://mlsanity.com/paper/1612.07003/) (2016)
- [C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/1912.09628/) (2019)
- [From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities](https://mlsanity.com/paper/1808.07954/) (2018)
- [Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining](https://mlsanity.com/paper/1711.08477/) (2017)
- [Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease](https://mlsanity.com/paper/2012.08769/) (2020)
- [Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach](https://mlsanity.com/paper/2010.06824/) (2020)

## Built on by (most cited first, 1 of 1)

- [Automated Machine Learning in Radiomics: A Comparative Evaluation of Performance, Efficiency and Accessibility](https://mlsanity.com/paper/2601.08334/) (2026)

## Abstract (arXiv)

Predicting clinical outcomes from medical images using quantitative features (``radiomics'') requires many method design choices, Currently, in new clinical applications, finding the optimal radiomics method out of the wide range of methods relies on a manual, heuristic trial-and-error process. We introduce a novel automated framework that optimizes radiomics workflow construction per application by standardizing the radiomics workflow in modular components, including a large collection of algorithms for each component, and formulating a combined algorithm selection and hyperparameter optimization problem. To solve it, we employ automated machine learning through two strategies (random search and Bayesian optimization) and three ensembling approaches. Results show that a medium-sized random search and straight-forward ensembling perform similar to more advanced methods while being more efficient. Validated across twelve clinical applications, our approach outperforms both a radiomics baseline and human experts. Concluding, our framework improves and streamlines radiomics research by fully automatically optimizing radiomics workflow construction. To facilitate reproducibility, we publicly release six datasets, software of the method, and code to reproduce this study.