Models capable of leveraging unlabelled data are crucial in overcoming large distribution gaps between the acquired datasets across different imaging devices and configurations. In this regard, self-training techniques based on pseudo-labeling have been shown to be highly effective for semi-supervised domain adaptation. However, the unreliability of pseudo labels can hinder the capability of self-training techniques to induce abstract representation from the unlabeled target dataset, especially in the case of large distribution gaps. Since the neural network performance should be invariant to image transformations, we look to this fact to identify uncertain pseudo labels. Indeed, we argue that transformation invariant detections can provide more reasonable approximations of ground truth. Accordingly, we propose a semi-supervised learning strategy for domain adaptation termed transformation-invariant self-training (TI-ST). The proposed method assesses pixel-wise pseudo-labels' reliability and filters out unreliable detections during self-training. We perform comprehensive evaluations for domain adaptation using three different modalities of medical images, two different network architectures, and several alternative state-of-the-art domain adaptation methods. Experimental results confirm the superiority of our proposed method in mitigating the lack of target domain annotation and boosting segmentation performance in the target domain.

翻译：能够利用无标注数据的模型对于克服不同成像设备和配置下采集数据集之间的巨大分布差异至关重要。在这方面，基于伪标签的自训练技术已被证明在半监督域适应中非常有效。然而，伪标签的不可靠性会限制自训练技术从无标注目标数据集中提取抽象表示的能力，特别是在分布差异较大的情况下。由于神经网络性能应对图像变换具有不变性，我们利用这一特性来识别不可靠的伪标签。具体而言，我们认为变换不变检测能够提供更合理的真实标注近似。因此，我们提出一种用于域适应的半监督学习策略——变换不变自训练（TI-ST）。该方法评估逐像素伪标签的可靠性，并在自训练过程中过滤掉不可靠的检测结果。我们使用三种不同模态的医学图像、两种不同的网络架构以及多种前沿的域适应方法进行了全面的域适应评估。实验结果表明，我们提出的方法在缓解目标域标注缺失问题和提升目标域分割性能方面具有优越性。