2022-02-01
Streamlined Quantitative Imaging Biomarker Development: Generalization of radiomics through automated machine learning
Publication
Publication
Gestroomlijnde ontwikkeling van kwantitatieve biomarkers op basis van beeldvorming: Generalisatie van radiomics door automatische machine learning
Radiomics uses quantitative medical imaging features and AI to create predictive models which can be used as biomarkers. In this thesis, we have developped an adaptive radiomics framework to automatically optimize the radiomics workflow per application and demonstrate its use to create biomarkers in eight different clinical applications.
| Additional Metadata | |
|---|---|
| , , , , , , , , , , , , , | |
| This work is part of the research programme STRaTeGy with project numbers 14929, 14930, and 14932, which is (partly) financed by the Dutch Research Council (NWO). | |
| W.J. Niessen (Wiro) | |
| Erasmus University Rotterdam | |
| hdl.handle.net/1765/137089 | |
| ASCI dissertation series | |
| Organisation | Department of Radiology |
|
Starmans, M. (2022, February). Streamlined Quantitative Imaging Biomarker Development: Generalization of radiomics through automated machine learning (No. 431). ASCI dissertation series. Retrieved from http://hdl.handle.net/1765/137089 |
|
