Simplatab: An Automated Machine Learning Framework for Radiomics-Based Bi-Parametric MRI Detection of Clinically Significant Prostate Cancer
Our findings indicate that Simplatab can significantly enhance the usability, accountability, and explainability of machine learning in clinical settings, thereby increasing trust and accessibility for AI non-experts.
Authors
- Eugenia Mylona (ORCID: https://orcid.org/0000-0002-1275-6249)
- Charalampos Kalantzopoulos (ORCID: https://orcid.org/0000-0002-6705-5896)
- Kostas Marias (ORCID: https://orcid.org/0000-0003-3783-5223)
- Nikos Tsiknakis (ORCID: https://orcid.org/0000-0002-7581-467X)
- Dimitrios I. Fotiadis (ORCID: https://orcid.org/0000-0002-7362-5082)
- Nickolas Papanikolaou (ORCID: https://orcid.org/0000-0003-3298-2072)
- Nikolaos S. Tachos (ORCID: https://orcid.org/0000-0002-8627-6352)
- Dimitrios I. Zaridis (ORCID: https://orcid.org/0000-0003-2549-3887)
- Daniele Regge (ORCID: https://orcid.org/0000-0001-8267-5279)
- Vasileios C. Pezoulas (ORCID: https://orcid.org/0000-0002-1872-693X)
- Manolis Tsiknakis (ORCID: https://orcid.org/0000-0001-8454-1450)
- George K. Matsopoulos (ORCID: https://orcid.org/0000-0002-2600-9914)
Institutions
- Champalimaud Foundation (PT)
- National Technical University of Athens (GR)
- University of Ioannina (GR)
- Hellenic Mediterranean University (GR)
- FORTH Institute of Electronic Structure and Laser (GR)
- Candiolo Cancer Institute (IT)
- Foundation for Research and Technology Hellas (GR)
Publication Details
- Journal
- Bioengineering
- Published
- 2025-02-26
- DOI
- https://doi.org/10.3390/bioengineering12030242
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44