Centrala begrepp
Utilizing counterfactual image generation improves robustness and performance in contrastive learning for medical imaging.
Sammanfattning
Counterfactual contrastive learning enhances downstream task performance by incorporating domain-specific information through realistic image synthesis. The proposed CF-SimCLR method outperforms standard SimCLR by explicitly aligning domains in learned representations. Evaluation on chest radiography and mammography datasets shows significant improvements in robustness to acquisition shift, especially for under-represented domains. The lightweight counterfactual inference model used requires minimal computational overhead compared to the contrastive learning process.
Statistik
"Comprehensive evaluation across five datasets, on chest radiography and mammography."
"CF-SimCLR substantially improves robustness to acquisition shift with higher downstream performance."
"Generated domain counterfactuals can fool a domain classifier trained on real data 95% of the time."
Citat
"CF-SimCLR substantially improves robustness to acquisition shift with higher downstream performance."
"Counterfactual contrastive learning enhances downstream task performance by incorporating domain-specific information through realistic image synthesis."