Research map / Label-efficient and robust learning / Domain adaptation and generalization
Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine image adaptation and feature adaptation in a shared end-to-end framework | Cross-modality cardiac segmentation, MR→CT · Multi-Modality Whole Heart Segmentation Challenge 2017 | Average Dice: 17.2 -> 73.0 | Helped |
| Add image adaptation to the no-adaptation model (ablation) | Cross-modality cardiac segmentation, MR→CT · Multi-Modality Whole Heart Segmentation Challenge 2017 | Average Dice: 17.2 -> 58.0 | Helped |
| Add semantic prediction-space feature adaptation (FA-P) (ablation) | Image adaptation baseline · Multi-Modality Whole Heart Segmentation Challenge 2017 | Average Dice: 58.0% to 65.7% | Helped |
| Add generated-image-space feature adaptation (FA-I) (ablation) | Image adaptation + FA-P · Multi-Modality Whole Heart Segmentation Challenge 2017 | Average Dice: 65.7 -> 73.0 | Helped |
| Use image adaptation and feature adaptation together rather than image adaptation alone (ablation) | Cross-modality cardiac segmentation, MR→CT · Multi-Modality Whole Heart Segmentation Challenge 2017 | Average Dice: 58.0 -> 73.0 | Helped |
| Use SIFA instead of CyCADA | Cross-modality cardiac segmentation, MR→CT · Multi-Modality Whole Heart Segmentation Challenge 2017 | Average Dice: 64.4 -> 73.0 | Helped |
| Use SIFA instead of CyCADA | Cross-modality cardiac segmentation, MR→CT · Multi-Modality Whole Heart Segmentation Challenge 2017 | Average ASD: 9.4 -> 8.1 | Helped |
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