Automated skin lesion segmentation through dermoscopic analysis is essential for early skin cancer detection, yet remains challenging due to limited annotated training data. We present MIRA-U, a semi-supervised framework that combines uncertainty-aware teacher-student pseudo-labeling with a hybrid CNN-Transformer architecture. Our approach employs a teacher network pre-trained via masked image modeling to generate confidence-weighted soft pseudo-labels, which guide a U-shaped CNN-Transformer student network featuring cross-attention skip connections. This design enhances pseudo-label quality and boundary delineation, surpassing reconstruction-based and CNN-only baselines, particularly in low-annotation regimes. Extensive evaluation on ISIC-2016 and PH2 datasets demonstrates superior performance, achieving a Dice Similarity Coefficient (DSC) of 0.9153 and Intersection over Union (IoU) of 0.8552 using only 50% labeled data. Code is publicly available on GitHub.

翻译：通过皮肤镜分析实现自动化皮肤病变分割对于早期皮肤癌检测至关重要，但由于标注训练数据有限，该任务仍具挑战性。本文提出MIRA-U，一种结合不确定性感知师生伪标注与混合CNN-Transformer架构的半监督框架。该方法采用通过掩码图像建模预训练的教师网络生成置信度加权的软伪标签，用以指导具有跨注意力跳跃连接的U形CNN-Transformer学生网络。该设计提升了伪标签质量与边界描绘能力，超越了基于重建及纯CNN的基线方法，在低标注数据场景下表现尤为突出。在ISIC-2016和PH2数据集上的广泛评估展示了其优越性能，仅使用50%标注数据即可达到0.9153的戴斯相似系数（DSC）和0.8552的交并比（IoU）。代码已在GitHub上公开。