ZScribbleSeg maximizes scribble supervision with efficient annotation forms, spatial regularization, and EM-estimated class ratios to deliver competitive performance on six medical segmentation tasks without full labels.
nnU-Net: Breaking the Spell on Successful Medical Im age Segmentation
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Introduces Implantable Adaptive Cells inserted into pre-trained U-Nets via Partially-Connected DARTS to achieve approximately 5 percentage point gains in segmentation accuracy on four medical MRI/CT datasets.
citing papers explorer
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ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision
ZScribbleSeg maximizes scribble supervision with efficient annotation forms, spatial regularization, and EM-estimated class ratios to deliver competitive performance on six medical segmentation tasks without full labels.
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Implantable Adaptive Cells: A Novel Enhancement for Pre-Trained U-Nets in Medical Image Segmentation
Introduces Implantable Adaptive Cells inserted into pre-trained U-Nets via Partially-Connected DARTS to achieve approximately 5 percentage point gains in segmentation accuracy on four medical MRI/CT datasets.