The combination of semi-supervised learning (SemiSL) and contrastive learning (CL) has been successful in medical image segmentation with limited annotations. However, these works often rely on pretext tasks that lack the specificity required for pixel-level segmentation, and still face overfitting issues due to insufficient supervision signals resulting from too few annotations. Therefore, this paper proposes an affinity-graph-guided semi-supervised contrastive learning framework (Semi-AGCL) by establishing additional affinity-graph-based supervision signals between the student and teacher network, to achieve medical image segmentation with minimal annotations without pretext. The framework first designs an average-patch-entropy-driven inter-patch sampling method, which can provide a robust initial feature space without relying on pretext tasks. Furthermore, the framework designs an affinity-graph-guided loss function, which can improve the quality of the learned representation and the model generalization ability by exploiting the inherent structure of the data, thus mitigating overfitting. Our experiments indicate that with merely 10% of the complete annotation set, our model approaches the accuracy of the fully annotated baseline, manifesting a marginal deviation of only 2.52%. Under the stringent conditions where only 5% of the annotations are employed, our model exhibits a significant enhancement in performance surpassing the second best baseline by 23.09% on the dice metric and achieving an improvement of 26.57% on the notably arduous CRAG and ACDC datasets.

翻译：半监督学习与对比学习的结合在有限标注的医学图像分割中已取得成功。然而，现有方法通常依赖于缺乏像素级分割特异性的预训练任务，且因标注过少导致监督信号不足，仍面临过拟合问题。为此，本文提出一种亲和图引导的半监督对比学习框架，通过在学生网络与教师网络之间建立基于亲和图的额外监督信号，实现无需预训练任务的极简标注医学图像分割。该框架首先设计了一种基于平均块熵的块间采样方法，能够在不依赖预训练任务的前提下提供稳健的初始特征空间。此外，框架设计了亲和图引导的损失函数，通过挖掘数据内在结构提升学习表征的质量与模型泛化能力，从而缓解过拟合。实验表明，仅使用完整标注集10%的标注时，本模型即可接近全标注基线的精度，仅产生2.52%的微小偏差。在仅使用5%标注的严格条件下，本模型在Dice指标上显著优于次优基线23.09%，并在极具挑战性的CRAG与ACDC数据集上分别实现26.57%的性能提升。