Style-Aware Blending and Prototype-Based Cross-Contrast Consistency for Semi-Supervised Medical Image Segmentation

TL;DR

This paper introduces a novel semi-supervised medical image segmentation framework that uses style-aware blending and prototype-based cross-contrast consistency to address data stream separation and underutilization of supervisory information, improving segmentation performance.

Contribution

It proposes a style-guided distribution blending module and a prototype-based cross-contrast strategy to enhance semi-supervised segmentation by addressing key limitations of existing methods.

Findings

Outperforms existing semi-supervised methods on multiple benchmarks.

Effectively mitigates confirmation bias and noise in pseudo-labels.

Enhances utilization of supervisory signals in semi-supervised learning.

Abstract

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.