RADA achieves state-of-the-art barely-supervised 3D medical image segmentation by using a region-aware dual-encoder pre-trained on Alpha-CLIP within a triple-view training framework on LA2018, KiTS19 and LiTS datasets.
Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results
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MoASE++ combines activation sparsity experts with domain-adaptive on-policy distillation to achieve state-of-the-art continual test-time adaptation on image classification and segmentation benchmarks.
CardioMix uses cardiac pattern-guided bidirectional fusion to mix labeled and unlabeled ECG data for better semi-supervised segmentation while keeping samples physiologically valid.
citing papers explorer
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RADA: Region-Aware Dual-encoder Auxiliary learning for Barely-supervised Medical Image Segmentation
RADA achieves state-of-the-art barely-supervised 3D medical image segmentation by using a region-aware dual-encoder pre-trained on Alpha-CLIP within a triple-view training framework on LA2018, KiTS19 and LiTS datasets.
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MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation
MoASE++ combines activation sparsity experts with domain-adaptive on-policy distillation to achieve state-of-the-art continual test-time adaptation on image classification and segmentation benchmarks.
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Bidirectional Fusion Guided by Cardiac Patterns for Semi-Supervised ECG Segmentation
CardioMix uses cardiac pattern-guided bidirectional fusion to mix labeled and unlabeled ECG data for better semi-supervised segmentation while keeping samples physiologically valid.