Semantic Segmentation On Acdc Scribbles
Metrics
Dice (Average)
Results
Performance results of various models on this benchmark
Model Name | Dice (Average) | Paper Title | Repository |
|---|---|---|---|
| CycleMix | 84.8% | CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision | |
| ScribbleVC | 88.4% | ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding | |
| CutMix | 70.5% | CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features | |
| ScribFormer | 88.8% | ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation | |
| TFCNs | 64.5% | TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation | |
| Puzzle Mix | 62.4% | Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup |
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