C2W-Tune transfers weights from a pre-trained LA cavity model to achieve higher accuracy in thin atrial wall segmentation, raising Dice from 0.623 to 0.814 on the 2018 LA challenge dataset.
An Ensemble of 3D Residual Encoder UNet Models for Solving Multi - class Bi-atrial Segmentation Challenge,
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MAML with auxiliary cavity tasks and boundary loss improves 5-shot LA wall segmentation over standard fine-tuning (DSC 0.54 vs 0.48) and nears fully supervised performance at 20 shots.
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C2W-Tune: Cavity-to -Wall Transfer Learning for Thin Atrial Wall Segmentation in 3D LGE-MRI
C2W-Tune transfers weights from a pre-trained LA cavity model to achieve higher accuracy in thin atrial wall segmentation, raising Dice from 0.623 to 0.814 on the 2018 LA challenge dataset.
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Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning
MAML with auxiliary cavity tasks and boundary loss improves 5-shot LA wall segmentation over standard fine-tuning (DSC 0.54 vs 0.48) and nears fully supervised performance at 20 shots.