Research map / Label-efficient and robust learning / Semi-supervised learning
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use learned independent spatial and appearance transformations to synthesize training examples | One-shot brain MRI segmentation · Brain MRI held-out test set | mean Dice: 0.759 -> 0.804 | Helped |
| Add hand-tuned random augmentation to Ours-indep training (ablation) | Ours-indep · Brain MRI held-out test set | mean Dice: 0.804 -> 0.815 | Helped |
| Sample spatial and appearance transformations independently rather than from the same target scan (ablation) | Ours-coupled · Brain MRI held-out test set | mean Dice: 0.795 -> 0.804 | Helped |
| Augment the segmenter training set with SAS-labeled unlabeled scans | SAS · Brain MRI held-out test set | mean Dice: 0.759 -> 0.775 | Helped |
| Use hand-tuned random augmentation | One-shot brain MRI segmentation · Brain MRI held-out test set | mean Dice: 0.759 -> 0.765 | Helped |
| Use hand-tuned random augmentation | One-shot brain MRI segmentation · Large versus small brain structures | Dice | Mixed |
Closest papers by meaning
- Modeling the Probabilistic Distribution of Unlabeled Data for One-shot Medical Image Segmentation (AAAI 2021 · similarity 0.91)
- Medical Image Segmentation with Minimal Labeling Effort: How Far Can We Push the Limits? (AAAI 2026 · similarity 0.89)
- One-Prompt to Segment All Medical Images (CVPR 2024 · similarity 0.87)
- Generative Medical Segmentation (AAAI 2025 · similarity 0.87)
- Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation (CVPR 2024 · similarity 0.87)
- Combinatorial CNN-Transformer Learning with Manifold Constraints for Semi-supervised Medical Image Segmentation (AAAI 2024 · similarity 0.87)
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