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DBANet: Dual Boundary Awareness With Confidence-Guided Pseudo Labeling for Medical Image Segmentation

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Proposed approach improves segmentation accuracy in 3D medical images, indicating effective boundary management.

DBANet achieves an average Dice score improvement of 3.56% under 10% labeled data, enhancing segmentation accuracy.
By employing Boundary Distribution Awareness, DBANet tackles class imbalance effectively, leading to more reliable predictions.
The Confidence-Guided Pseudo-Label Fusion module boosts training stability and outcomes in semi-supervised learning scenarios.
Evaluation on two datasets confirms boundary-aware techniques significantly enhance accuracy in medical image segmentation.

Chen et al. (2025) studied this question.

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