Kavli Affiliate: Xiang Zhang
| First 5 Authors: Chaowei Chen, Chaowei Chen, , ,
| Summary:
Weak-strong consistency learning strategies are widely employed in
semi-supervised medical image segmentation to train models by leveraging
limited labeled data and enforcing weak-to-strong consistency. However,
existing methods primarily focus on designing and combining various
perturbation schemes, overlooking the inherent potential and limitations within
the framework itself. In this paper, we first identify two critical
deficiencies: (1) separated training data streams, which lead to confirmation
bias dominated by the labeled stream; and (2) incomplete utilization of
supervisory information, which limits exploration of strong-to-weak
consistency. To tackle these challenges, we propose a style-aware blending and
prototype-based cross-contrast consistency learning framework. Specifically,
inspired by the empirical observation that the distribution mismatch between
labeled and unlabeled data can be characterized by statistical moments, we
design a style-guided distribution blending module to break the independent
training data streams. Meanwhile, considering the potential noise in strong
pseudo-labels, we introduce a prototype-based cross-contrast strategy to
encourage the model to learn informative supervisory signals from both
weak-to-strong and strong-to-weak predictions, while mitigating the adverse
effects of noise. Experimental results demonstrate the effectiveness and
superiority of our framework across multiple medical segmentation benchmarks
under various semi-supervised settings.
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