Semantic Segmentation On Acdc Scribbles

Dice (Average)

평가 결과

이 벤치마크에서 각 모델의 성능 결과

		Paper Title
ScribFormer	88.8%	ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation
ScribbleVC	88.4%	ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding
CycleMix	84.8%	CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision
CutMix	70.5%	CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
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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