Biomarker Robustness
Cross-source consensus on Biomarker Robustness from 1 sources and 5 claims.
1 sources · 5 claims
Uses
How it works
Risks & contraindications
Evidence quality
Highlighted claims
- High classification accuracy alone cannot show that a model has learned robust or neurobiologically meaningful biomarkers. — Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity
- The findings interpret classification accuracy as inadequate for biomarker discovery. — Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity
- Models can achieve high accuracy while producing non-reproducible or weakly supported salient features. — Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity
- Future biomarker studies should assess robustness and neurobiological faithfulness instead of relying on classification performance. — Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity
- Cross-entropy optimization does not force models to prioritize neurobiologically faithful features. — Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity