无需标注，自动校准边界，实现低资源皮肤病变分割
RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy
- 通过伪标签可靠性学习与边界校准，实现无监督分割
- 在三个数据集上达86.58%平均DICE，仅更新3.5%参数
- 适合医疗影像、资源受限场景的快速部署
像素级标注在低资源皮肤镜图像中成本高昂。我们提出RABC-Net，一种可靠性感知的无标注分割系统，结合伪标签可靠性学习、受限目标域自适应和可靠性自适应边界校准（RABC）。该系统将可靠性学习与推理解耦：训练时通过不确定性感知的伪标签交互构建鲁棒表征，推理时仅需图像输入，RABC基于边界置信度、不确定性与前景概率进行局部逻辑空间校准。训练与域自适应均不使用人工掩码；验证标签仅用于最终工作点选择。在ISIC-2017、ISIC-2018和PH2数据集上，RABC-Net取得86.58%的宏平均DICE和79.47%的宏平均JAC，结果一致。受控分析表明，RABC在局部边界上优于非学习校准方法，整体性能来自完整可靠性感知系统。域自适应仅更新3.50%模型参数，图像推理速度达87.4 FPS，且所有数据集最优工作点均采用σ=0，说明学习到的校准避免了部署时额外平滑。
原文摘要 · Abstract (English)
Pixel-level annotation is costly in low-resource dermoscopy. We present RABC-Net, a reliability-aware annotation-free segmentation system that combines pseudo-label reliability learning, restricted target-domain adaptation, and Reliability-Adaptive Boundary Calibration (RABC). The system decouples reliability learning from deployment: uncertainty-aware pseudo-label interaction shapes robust representations during training, while the image-only inference path is preserved and RABC performs local logit-space calibration from boundary confidence, uncertainty, and foreground probability. No manual masks are used for training or target-domain adaptation; validation labels, when available, are used only for final operating-point selection. Across ISIC-2017, ISIC-2018, and PH2, RABC-Net achieves macro-average DICE/JAC of 86.58\%/79.47\% and consistent matched-protocol results. Controlled within-study analyses show that RABC provides localized gains over nonlearned boundary correction, while the overall result comes from the full reliability-aware system. Adaptation updates only 3.50\% of model parameters, image-only inference runs at 87.4 FPS, and the selected operating points use $σ=0$ on all three datasets, indicating that learned calibration avoids extra smoothing at deployment.
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