Automated Machine Learning Frameworks for Radiomics: Comparative Evaluation Study
Jose Lozano-Montoya, Emilio Soria-Olivas, Almudena Fuster-Matanzo, Angel Alberich-Bayarri, Ana Jimenez-Pastor, Javad Sarvestan, Shahram Taeb · 2026
Read Automated Machine Learning Frameworks for Radiomics: Comparative Evaluation Study by Jose Lozano-Montoya, Emilio Soria-Olivas, Almudena Fuster-Matanzo, Angel Alberich-Bayarri, Ana Jimenez-Pastor, Javad Sarvestan, Shahram Taeb (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13305470.txt.
Description
Jose Lozano-Montoya and a team of co-authors compared self building machine learning tools for radiomics. Their study, from twenty twenty-six, deals with models that read scan data to forecast health outcomes. These auto tools let staff with no code skill build such models. Yet how well they cope with scan data quirks was not clear. The team set out to test speed, scores, and ease of use.
They ran six tools on ten data sets, both public and private. The sets mixed scan types, sizes, body parts, and end points. Readers who work with scans learn which tools suit which tasks. The study also flags where tool makers should improve for scan based work.
Catalog record
| CREATOR | Jose Lozano-Montoya, Emilio Soria-Olivas, Almudena Fuster-Matanzo, Angel Alberich-Bayarri, Ana Jimenez-Pastor, Javad Sarvestan, Shahram Taeb |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 43 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |