Abstract
Annotation-free skin lesion segmentation is attractive for low-resource dermoscopic deployment. However, its performance remains constrained by three coupled challenges: noisy pseudo-label supervision, unstable transfer under limited target-domain data, and boundary probability under-confidence. Most existing annotation-free methods primarily focus on pseudo-label denoising. In contrast, the effect of compressed boundary probabilities on final mask quality has received less explicit attention, although it directly affects contour completeness and cannot be adequately corrected by global threshold adjustment alone. To address this issue, we propose BPC-Net, a boundary probability calibration framework for annotation-free skin lesion segmentation. The core of the framework is Gaussian Probability Smoothing (GPS), which performs localized probability-space calibration before thresholding to recover under-confident lesion boundaries without inducing indiscriminate foreground expansion. To support this calibration under noisy pseudo-supervision and cross-domain transfer, we further incorporate two auxiliary designs: a feature-decoupled decoder that separately handles context suppression, detail recovery, and boundary refinement, and an interaction-branch adaptation strategy that updates only the pseudo-label interaction branch while preserving the deployed image-only segmentation path. Under a strictly annotation-free protocol, no manual masks are used during training or target-domain adaptation, and validation labels, when available, are used only for final operating-point selection. Experiments on ISIC-2017, ISIC-2018, and PH2 show that the proposed framework achieves state-of-the-art performance among published unsupervised methods, reaching a macro-average Dice coefficient and Jaccard index of 85.80\% and 76.97\%, respectively, while approaching supervised reference performance on PH2.
Abstract (translated)
无标注皮肤病变分割因其适用于低资源皮肤镜部署而备受关注。然而，其性能仍受限于三个相互关联的挑战：噪声伪标签监督、有限目标域数据下的不稳定迁移，以及边界概率欠置信。大多数现有无标注方法主要聚焦于伪标签去噪。相比之下，压缩的边界概率对最终掩码质量的影响较少受到明确关注——尽管它直接影响轮廓完整性，且无法仅通过全局阈值调整充分修正。针对此问题，我们提出BPC-Net，一种面向无标注皮肤病变分割的边界概率校准框架。该框架的核心是高斯概率平滑（GPS），它在阈值化之前执行局部概率空间校准，以恢复欠置信的病变边界，同时避免 indiscriminate 前景扩张。为在噪声伪监督和跨域迁移下支持此校准，我们进一步引入两种辅助设计：一种分别处理上下文抑制、细节恢复和边界细化的特征解耦解码器，以及一种仅更新伪标签交互分支同时保留部署的纯图像分割路径的交互分支自适应策略。在严格的无标注协议下，训练和目标域适应过程中均不使用人工标注掩码，验证标签（若可用）仅用于最终操作点选择。在ISIC-2017、ISIC-2018和PH2数据集上的实验表明，所提框架在已发表的无监督方法中达到最先进性能，宏平均Dice系数和Jaccard指数分别达到85.80%和76.97%，同时在PH2上接近有监督参考性能。
URL
https://arxiv.org/abs/2604.05594
